Personalizing Default Search Queries on Online Social Networks

ABSTRACT

In one embodiment, a method includes scoring a set of content objects based on one or more user-engagement factors, identifying one or more related content objects, wherein each related content objects is connected within the online social network to one or more content objects of the set of content objects having a score greater than a threshold score, generating a plurality of structured queries that each comprise references to one or more content objects, wherein at least one of the structured queries is a personalized query comprising a reference to at least one of the related content objects, and sending instructions to a client device for presenting one or more of the generated structured queries to a first user for display on an interface currently accessed by the first user, wherein at least one of the sent structured queries is a personalized query.

PRIORITY

This application is a continuation under 35 U.S.C. §120 of U.S. patentapplication Ser. No. 14/449,291, filed 1 Aug. 2014, which is acontinuation-in-part under 35 U.S.C. §120 of U.S. patent applicationSer. No. 14/052,536, filed 11 Oct. 2013, issued as U.S. Pat. No.9,262,482 on 16 Feb. 2016, which is a continuation-in-part under 35U.S.C. §120 of U.S. patent application Ser. No. 13/732,175, filed 31Dec. 2012 issued as U.S. Pat. No. 8,918,418 on 23 Dec. 2014, which is acontinuation-in-part under 35 U.S.C. §120 of U.S. patent applicationSer. No. 13/556,046, filed 23 Jul. 2012, issued as U.S. Pat. No.8,751,521 on 10 Jun. 2014, which is a continuation-in-part under 35U.S.C. §120 of U.S. patent application Ser. No. 12/763,162, filed 19Apr. 2010, issued as U.S. Pat. No. 8,572,129 on 29 Oct. 2013, each ofwhich is incorporated by reference.

TECHNICAL FIELD

This disclosure generally relates to social graphs and performingsearches for objects within a social-networking environment.

BACKGROUND

A social-networking system, which may include a social-networkingwebsite, may enable its users (such as persons or organizations) tointeract with it and with each other through it. The social-networkingsystem may, with input from a user, create and store in thesocial-networking system a user profile associated with the user. Theuser profile may include demographic information, communication-channelinformation, and information on personal interests of the user. Thesocial-networking system may also, with input from a user, create andstore a record of relationships of the user with other users of thesocial-networking system, as well as provide services (e.g. wall posts,photo-sharing, event organization, messaging, games, or advertisements)to facilitate social interaction between or among users.

The social-networking system may send over one or more networks contentor messages related to its services to a mobile or other computingdevice of a user. A user may also install software applications on amobile or other computing device of the user for accessing a userprofile of the user and other data within the social-networking system.The social-networking system may generate a personalized set of contentobjects to display to a user, such as a newsfeed of aggregated storiesof other users connected to the user.

Social-graph analysis views social relationships in terms of networktheory consisting of nodes and edges. Nodes represent the individualactors within the networks, and edges represent the relationshipsbetween the actors. The resulting graph-based structures are often verycomplex. There can be many types of nodes and many types of edges forconnecting nodes. In its simplest form, a social graph is a map of allof the relevant edges between all the nodes being studied.

SUMMARY OF PARTICULAR EMBODIMENTS

In particular embodiments, a social-networking system may generatestructured queries that include references to particular social-graphelements. These structured queries may be generated, for example, inresponse to a text query provided by a user, or generated as defaultqueries. By providing suggested structured queries to a user's textquery, the social-networking system may provide a powerful way for usersof an online social network to search for elements represented in asocial graph based on their social-graph attributes and their relationto various social-graph elements.

In particular embodiments, the social-networking system may generate aset of personalized structured queries for a page of the online socialnetwork. These suggested structured queries for a user based onuser-engagement factors. It may be desirable for the social-networkingsystem to provide users with structured queries that are moreinteresting to the user and thus more likely to be engaged with (e.g.,selected or clicked-thru). It may further be desirable to providepersonalized suggestions for queries based on information associatedwith the querying user and the user's friend (e.g., social-graphinformation). Search results can be identified that can be of interestto the user. Queries can be suggested that will return a search-resultspage comprising the identified results. A variety of factors may be usedto increase user engagement, such as, for example,business-intelligence, user-preference/history, social-graph affinity,advertising sponsorship, or other suitable factors may be used todetermine which suggested queries to serve to the user. Thesocial-networking system may calculate which structured queries are morelikely to be engaged with by a user, and then send suggested queries tothe user that the user is more likely to engage with.

In particular embodiments, the social networking system may generatepersonalized search queries to the user. The social networking systemmay identify a first set of nodes and score those nodes based onengagement factors. The social networking system can then identifycommon nodes that connect multiple nodes from the first set of nodes.The social networking system can then generate structured queries thatreference nodes and edges, where at least one structured query is apersonalized query. The social networking system can then send thepersonalized search query to the first user for display.

The embodiments disclosed above are only examples, and the scope of thisdisclosure is not limited to them. Particular embodiments may includeall, some, or none of the components, elements, features, functions,operations, or steps of the embodiments disclosed above.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example network environment associated with asocial-networking system.

FIG. 2 illustrates an example social graph.

FIG. 3 illustrates an example page of an online social network.

FIGS. 4A-4B illustrate example queries of the social network.

FIG. 5 illustrates example personalized search queries of the onlinesocial network.

FIG. 6 illustrates an example page of an online social network withpersonalized search queries.

FIG. 7 illustrates an example method for generating personalized defaultsearch queries for a user.

FIG. 8 illustrates an example system for generating personalized defaultsearch queries for a user.

FIG. 9 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS System Overview

FIG. 1 illustrates an example network environment 100 associated with asocial-networking system. Network environment 100 includes a clientsystem 130, a social-networking system 160, and a third-party system 170connected to each other by a network 110. Although FIG. 1 illustrates aparticular arrangement of client system 130, social-networking system160, third-party system 170, and network 110, this disclosurecontemplates any suitable arrangement of client system 130,social-networking system 160, third-party system 170, and network 110.As an example and not by way of limitation, two or more of client system130, social-networking system 160, and third-party system 170 may beconnected to each other directly, bypassing network 110. As anotherexample, two or more of client system 130, social-networking system 160,and third-party system 170 may be physically or logically co-locatedwith each other in whole or in part. Moreover, although FIG. 1illustrates a particular number of client systems 130, social-networkingsystems 160, third-party systems 170, and networks 110, this disclosurecontemplates any suitable number of client systems 130,social-networking systems 160, third-party systems 170, and networks110. As an example and not by way of limitation, network environment 100may include multiple client system 130, social-networking systems 160,third-party systems 170, and networks 110.

This disclosure contemplates any suitable network 110. As an example andnot by way of limitation, one or more portions of network 110 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a cellular telephone network, or a combinationof two or more of these. Network 110 may include one or more networks110.

Links 150 may connect client system 130, social-networking system 160,and third-party system 170 to communication network 110 or to eachother. This disclosure contemplates any suitable links 150. Inparticular embodiments, one or more links 150 include one or morewireline (such as for example Digital Subscriber Line (DSL) or Data OverCable Service Interface Specification (DOCSIS)), wireless (such as forexample Wi-Fi or Worldwide Interoperability for Microwave Access(WiMAX)), or optical (such as for example Synchronous Optical Network(SONET) or Synchronous Digital Hierarchy (SDH)) links. In particularembodiments, one or more links 150 each include an ad hoc network, anintranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, aportion of the Internet, a portion of the PSTN, a cellulartechnology-based network, a satellite communications technology-basednetwork, another link 150, or a combination of two or more such links150. Links 150 need not necessarily be the same throughout networkenvironment 100. One or more first links 150 may differ in one or morerespects from one or more second links 150.

In particular embodiments, client system 130 may be an electronic deviceincluding hardware, software, or embedded logic components or acombination of two or more such components and capable of carrying outthe appropriate functionalities implemented or supported by clientsystem 130. As an example and not by way of limitation, a client system130 may include a computer system such as a desktop computer, notebookor laptop computer, netbook, a tablet computer, e-book reader, GPSdevice, camera, personal digital assistant (PDA), handheld electronicdevice, cellular telephone, smartphone, other suitable electronicdevice, or any suitable combination thereof. This disclosurecontemplates any suitable client systems 130. A client system 130 mayenable a network user at client system 130 to access network 110. Aclient system 130 may enable its user to communicate with other users atother client systems 130.

In particular embodiments, client system 130 may include a web browser132, such as MICROSOFT INTERNET EXPLORER, GOOGLE CHROME or MOZILLAFIREFOX, and may have one or more add-ons, plug-ins, or otherextensions, such as TOOLBAR or YAHOO TOOLBAR. A user at client system130 may enter a Uniform Resource Locator (URL) or other addressdirecting the web browser 132 to a particular server (such as server162, or a server associated with a third-party system 170), and the webbrowser 132 may generate a Hyper Text Transfer Protocol (HTTP) requestand communicate the HTTP request to server. The server may accept theHTTP request and communicate to client system 130 one or more Hyper TextMarkup Language (HTML) files responsive to the HTTP request. Clientsystem 130 may render a webpage based on the HTML files from the serverfor presentation to the user. This disclosure contemplates any suitablepage files, including webpages or pages presented as a user interface ofa native application. As an example and not by way of limitation,webpages may render from HTML files, Extensible Hyper Text MarkupLanguage (XHTML) files, or Extensible Markup Language (XML) files,according to particular needs. Such pages may also execute scripts suchas, for example and without limitation, those written in JAVASCRIPT,JAVA, MICROSOFT SILVERLIGHT, combinations of markup language and scriptssuch as AJAX (Asynchronous JAVASCRIPT and XML), and the like. Herein,reference to a webpage encompasses one or more corresponding webpagefiles (which a browser may use to render the webpage) and vice versa,where appropriate.

In particular embodiments, social-networking system 160 may be anetwork-addressable computing system that can host an online socialnetwork. Social-networking system 160 may generate, store, receive, andsend social-networking data, such as, for example, user-profile data,concept-profile data, social-graph information, or other suitable datarelated to the online social network. Social-networking system 160 maybe accessed by the other components of network environment 100 eitherdirectly or via network 110. In particular embodiments,social-networking system 160 may include one or more servers 162. Eachserver 162 may be a unitary server or a distributed server spanningmultiple computers or multiple datacenters. Servers 162 may be ofvarious types, such as, for example and without limitation, web server,news server, mail server, message server, advertising server, fileserver, application server, exchange server, database server, proxyserver, another server suitable for performing functions or processesdescribed herein, or any combination thereof. In particular embodiments,each server 162 may include hardware, software, or embedded logiccomponents or a combination of two or more such components for carryingout the appropriate functionalities implemented or supported by server162. In particular embodiments, social-networking system 164 may includeone or more data stores 164. Data stores 164 may be used to storevarious types of information. In particular embodiments, the informationstored in data stores 164 may be organized according to specific datastructures. In particular embodiments, each data store 164 may be arelational database. Particular embodiments may provide interfaces thatenable a client system 130, a social-networking system 160, or athird-party system 170 to manage, retrieve, modify, add, or delete, theinformation stored in data store 164.

In particular embodiments, social-networking system 160 may store one ormore social graphs in one or more data stores 164. In particularembodiments, a social graph may include multiple nodes—which may includemultiple user nodes (each corresponding to a particular user) ormultiple concept nodes (each corresponding to a particular concept)—andmultiple edges connecting the nodes. Social-networking system 160 mayprovide users of the online social network the ability to communicateand interact with other users. In particular embodiments, users may jointhe online social network via social-networking system 160 and then addconnections (i.e., relationships) to a number of other users ofsocial-networking system 160 whom they want to be connected to. Herein,the term “friend” may refer to any other user of social-networkingsystem 160 with whom a user has formed a connection, association, orrelationship via social-networking system 160.

In particular embodiments, social-networking system 160 may provideusers with the ability to take actions on various types of items orobjects, supported by social-networking system 160. As an example andnot by way of limitation, the items and objects may include groups orsocial networks to which users of social-networking system 160 maybelong, events or calendar entries in which a user might be interested,computer-based applications that a user may use, transactions that allowusers to buy or sell items via the service, interactions withadvertisements that a user may perform, or other suitable items orobjects. A user may interact with anything that is capable of beingrepresented in social-networking system 160 or by an external system ofthird-party system 170, which is separate from social-networking system160 and coupled to social-networking system 160 via a network 110.

In particular embodiments, social-networking system 160 may be capableof linking a variety of entities. As an example and not by way oflimitation, social-networking system 160 may enable users to interactwith each other as well as receive content from third-party systems 170or other entities, or to allow users to interact with these entitiesthrough an application programming interfaces (API) or othercommunication channels.

In particular embodiments, a third-party system 170 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., that servers may communicate with. A third-party system 170 may beoperated by a different entity from an entity operatingsocial-networking system 160. In particular embodiments, however,social-networking system 160 and third-party systems 170 may operate inconjunction with each other to provide social-networking services tousers of social-networking system 160 or third-party systems 170. Inthis sense, social-networking system 160 may provide a platform, orbackbone, which other systems, such as third-party systems 170, may useto provide social-networking services and functionality to users acrossthe Internet.

In particular embodiments, a third-party system 170 may include athird-party content object provider. A third-party content objectprovider may include one or more sources of content objects, which maybe communicated to a client system 130. As an example and not by way oflimitation, content objects may include information regarding things oractivities of interest to the user, such as, for example, movie showtimes, movie reviews, restaurant reviews, restaurant menus, productinformation and reviews, or other suitable information. As anotherexample and not by way of limitation, content objects may includeincentive content objects, such as coupons, discount tickets, giftcertificates, or other suitable incentive objects.

In particular embodiments, social-networking system 160 also includesuser-generated content objects, which may enhance a user's interactionswith social-networking system 160. User-generated content may includeanything a user can add, upload, send, or “post” to social-networkingsystem 160. As an example and not by way of limitation, a usercommunicates posts to social-networking system 160 from a client system130. Posts may include data such as status updates or other textualdata, location information, photos, videos, links, music or othersimilar data or media. Content may also be added to social-networkingsystem 160 by a third-party through a “communication channel,” such as anewsfeed or stream.

In particular embodiments, social-networking system 160 may include avariety of servers, sub-systems, programs, modules, logs, and datastores. In particular embodiments, social-networking system 160 mayinclude one or more of the following: a web server, action logger,API-request server, relevance-and-ranking engine, content-objectclassifier, notification controller, action log,third-party-content-object-exposure log, inference module,authorization/privacy server, search module, ad-targeting module,user-interface module, user-profile store, connection store, third-partycontent store, or location store. Social-networking system 160 may alsoinclude suitable components such as network interfaces, securitymechanisms, load balancers, failover servers,management-and-network-operations consoles, other suitable components,or any suitable combination thereof. In particular embodiments,social-networking system 160 may include one or more user-profile storesfor storing user profiles. A user profile may include, for example,biographic information, demographic information, behavioral information,social information, or other types of descriptive information, such aswork experience, educational history, hobbies or preferences, interests,affinities, or location. Interest information may include interestsrelated to one or more categories. Categories may be general orspecific. As an example and not by way of limitation, if a user “likes”an article about a brand of shoes the category may be the brand, or thegeneral category of “shoes” or “clothing.” A connection store may beused for storing connection information about users. The connectioninformation may indicate users who have similar or common workexperience, group memberships, hobbies, educational history, or are inany way related or share common attributes. The connection informationmay also include user-defined connections between different users andcontent (both internal and external). A web server may be used forlinking social-networking system 160 to one or more client systems 130or one or more third-party system 170 via network 110. The web servermay include a mail server or other messaging functionality for receivingand routing messages between social-networking system 160 and one ormore client systems 130. An API-request server may allow a third-partysystem 170 to access information from social-networking system 160 bycalling one or more APIs. An action logger may be used to receivecommunications from a web server about a user's actions on or offsocial-networking system 160. In conjunction with the action log, athird-party-content-object log may be maintained of user exposures tothird-party-content objects. A notification controller may provideinformation regarding content objects to a client system 130.Information may be pushed to a client system 130 as notifications, orinformation may be pulled from client system 130 responsive to a requestreceived from client system 130. Authorization servers may be used toenforce one or more privacy settings of the users of social-networkingsystem 160. A privacy setting of a user determines how particularinformation associated with a user can be shared. The authorizationserver may allow users to opt in or opt out of having their actionslogged by social-networking system 160 or shared with other systems(e.g., third-party system 170), such as, for example, by settingappropriate privacy settings. Third-party-content-object stores may beused to store content objects received from third parties, such as athird-party system 170. Location stores may be used for storing locationinformation received from client systems 130 associated with users.Ad-pricing modules may combine social information, the current time,location information, or other suitable information to provide relevantadvertisements, in the form of notifications, to a user.

Social Graphs

FIG. 2 illustrates example social graph 200. In particular embodiments,social-networking system 160 may store one or more social graphs 200 inone or more data stores. In particular embodiments, social graph 200 mayinclude multiple nodes—which may include multiple user nodes 202 ormultiple concept nodes 204—and multiple edges 206 connecting the nodes.Example social graph 200 illustrated in FIG. 2 is shown, for didacticpurposes, in a two-dimensional visual map representation. In particularembodiments, a social-networking system 160, client system 130, orthird-party system 170 may access social graph 200 and relatedsocial-graph information for suitable applications. The nodes and edgesof social graph 200 may be stored as data objects, for example, in adata store (such as a social-graph database). Such a data store mayinclude one or more searchable or queryable indexes of nodes or edges ofsocial graph 200.

In particular embodiments, a user node 202 may correspond to a user ofsocial-networking system 160. As an example and not by way oflimitation, a user may be an individual (human user), an entity (e.g.,an enterprise, business, or third-party application), or a group (e.g.,of individuals or entities) that interacts or communicates with or oversocial-networking system 160. In particular embodiments, when a userregisters for an account with social-networking system 160,social-networking system 160 may create a user node 202 corresponding tothe user, and store the user node 202 in one or more data stores. Usersand user nodes 202 described herein may, where appropriate, refer toregistered users and user nodes 202 associated with registered users. Inaddition or as an alternative, users and user nodes 202 described hereinmay, where appropriate, refer to users that have not registered withsocial-networking system 160. In particular embodiments, a user node 202may be associated with information provided by a user or informationgathered by various systems, including social-networking system 160. Asan example and not by way of limitation, a user may provide his or hername, profile picture, contact information, birth date, sex, maritalstatus, family status, employment, education background, preferences,interests, or other demographic information. In particular embodiments,a user node 202 may be associated with one or more data objectscorresponding to information associated with a user. In particularembodiments, a user node 202 may correspond to one or more pages.

In particular embodiments, a concept node 204 may correspond to aconcept. As an example and not by way of limitation, a concept maycorrespond to a place (such as, for example, a movie theater,restaurant, landmark, or city); a website (such as, for example, awebsite associated with social-networking system 160 or a third-partywebsite associated with a web-application server); an entity (such as,for example, a person, business, group, sports team, or celebrity); aresource (such as, for example, an audio file, video file, digitalphoto, text file, structured document, or application) which may belocated within social-networking system 160 or on an external server,such as a web-application server; real or intellectual property (suchas, for example, a sculpture, painting, movie, game, song, idea,photograph, or written work); a game; an activity; an idea or theory;another suitable concept; or two or more such concepts. A concept node204 may be associated with information of a concept provided by a useror information gathered by various systems, including social-networkingsystem 160. As an example and not by way of limitation, information of aconcept may include a name or a title; one or more images (e.g., animage of the cover page of a book); a location (e.g., an address or ageographical location); a website (which may be associated with a URL);contact information (e.g., a phone number or an email address); othersuitable concept information; or any suitable combination of suchinformation. In particular embodiments, a concept node 204 may beassociated with one or more data objects corresponding to informationassociated with concept node 204. In particular embodiments, a conceptnode 204 may correspond to one or more pages.

In particular embodiments, a node in social graph 200 may represent orbe represented by a page (which may be referred to as a “profile page”).Profile pages may be hosted by or accessible to social-networking system160. Profile pages may also be hosted on third-party websites associatedwith a third-party server 170. As an example and not by way oflimitation, a profile page corresponding to a particular externalwebpage may be the particular external webpage and the profile page maycorrespond to a particular concept node 204. Profile pages may beviewable by all or a selected subset of other users. As an example andnot by way of limitation, a user node 202 may have a correspondinguser-profile page in which the corresponding user may add content, makedeclarations, or otherwise express himself or herself. As anotherexample and not by way of limitation, a concept node 204 may have acorresponding concept-profile page in which one or more users may addcontent, make declarations, or express themselves, particularly inrelation to the concept corresponding to concept node 204.

In particular embodiments, a concept node 204 may represent athird-party webpage or resource hosted by a third-party system 170. Thethird-party webpage or resource may include, among other elements,content, a selectable or other icon, or other inter-actable object(which may be implemented, for example, in JavaScript, AJAX, or PHPcodes) representing an action or activity. As an example and not by wayof limitation, a third-party webpage may include a selectable icon suchas “like,” “check in,” “eat,” “recommend,” or another suitable action oractivity. A user viewing the third-party webpage may perform an actionby selecting one of the icons (e.g., “eat”), causing a client system 130to send to social-networking system 160 a message indicating the user'saction. In response to the message, social-networking system 160 maycreate an edge (e.g., an “eat” edge) between a user node 202corresponding to the user and a concept node 204 corresponding to thethird-party webpage or resource and store edge 206 in one or more datastores.

In particular embodiments, a pair of nodes in social graph 200 may beconnected to each other by one or more edges 206. An edge 206 connectinga pair of nodes may represent a relationship between the pair of nodes.In particular embodiments, an edge 206 may include or represent one ormore data objects or attributes corresponding to the relationshipbetween a pair of nodes. As an example and not by way of limitation, afirst user may indicate that a second user is a “friend” of the firstuser. In response to this indication, social-networking system 160 maysend a “friend request” to the second user. If the second user confirmsthe “friend request,” social-networking system 160 may create an edge206 connecting the first user's user node 202 to the second user's usernode 202 in social graph 200 and store edge 206 as social-graphinformation in one or more of data stores 24. In the example of FIG. 2,social graph 200 includes an edge 206 indicating a friend relationbetween user nodes 202 of user “A” and user “B” and an edge indicating afriend relation between user nodes 202 of user “C” and user “B.”Although this disclosure describes or illustrates particular edges 206with particular attributes connecting particular user nodes 202, thisdisclosure contemplates any suitable edges 206 with any suitableattributes connecting user nodes 202. As an example and not by way oflimitation, an edge 206 may represent a friendship, family relationship,business or employment relationship, fan relationship, followerrelationship, visitor relationship, subscriber relationship,superior/subordinate relationship, reciprocal relationship,non-reciprocal relationship, another suitable type of relationship, ortwo or more such relationships. Moreover, although this disclosuregenerally describes nodes as being connected, this disclosure alsodescribes users or concepts as being connected. Herein, references tousers or concepts being connected may, where appropriate, refer to thenodes corresponding to those users or concepts being connected in socialgraph 200 by one or more edges 206.

In particular embodiments, an edge 206 between a user node 202 and aconcept node 204 may represent a particular action or activity performedby a user associated with user node 202 toward a concept associated witha concept node 204. As an example and not by way of limitation, asillustrated in FIG. 2, a user may “like,” “attended,” “played,”“listened,” “cooked,” “worked at,” or “watched” a concept, each of whichmay correspond to a edge type or subtype. A concept-profile pagecorresponding to a concept node 204 may include, for example, aselectable “check in” icon (such as, for example, a clickable “check in”icon) or a selectable “add to favorites” icon. Similarly, after a userclicks these icons, social-networking system 160 may create a “favorite”edge or a “check in” edge in response to a user's action correspondingto a respective action. As another example and not by way of limitation,a user (user “C”) may listen to a particular song (“Imagine”) using aparticular application (SPOTIFY, which is an online music application).In this case, social-networking system 160 may create a “listened” edge206 and a “used” edge (as illustrated in FIG. 2) between user nodes 202corresponding to the user and concept nodes 204 corresponding to thesong and application to indicate that the user listened to the song andused the application. Moreover, social-networking system 160 may createa “played” edge 206 (as illustrated in FIG. 2) between concept nodes 204corresponding to the song and the application to indicate that theparticular song was played by the particular application. In this case,“played” edge 206 corresponds to an action performed by an externalapplication (SPOTIFY) on an external audio file (the song “Imagine”).Although this disclosure describes particular edges 206 with particularattributes connecting user nodes 202 and concept nodes 204, thisdisclosure contemplates any suitable edges 206 with any suitableattributes connecting user nodes 202 and concept nodes 204. Moreover,although this disclosure describes edges between a user node 202 and aconcept node 204 representing a single relationship, this disclosurecontemplates edges between a user node 202 and a concept node 204representing one or more relationships. As an example and not by way oflimitation, an edge 206 may represent both that a user likes and hasused at a particular concept. Alternatively, another edge 206 mayrepresent each type of relationship (or multiples of a singlerelationship) between a user node 202 and a concept node 204 (asillustrated in FIG. 2 between user node 202 for user “E” and conceptnode 204 for “SPOTIFY”).

In particular embodiments, social-networking system 160 may create anedge 206 between a user node 202 and a concept node 204 in social graph200. As an example and not by way of limitation, a user viewing aconcept-profile page (such as, for example, by using a web browser or aspecial-purpose application hosted by the user's client system 130) mayindicate that he or she likes the concept represented by the conceptnode 204 by clicking or selecting a “Like” icon, which may cause theuser's client system 130 to send to social-networking system 160 amessage indicating the user's liking of the concept associated with theconcept-profile page. In response to the message, social-networkingsystem 160 may create an edge 206 between user node 202 associated withthe user and concept node 204, as illustrated by “like” edge 206 betweenthe user and concept node 204. In particular embodiments,social-networking system 160 may store an edge 206 in one or more datastores. In particular embodiments, an edge 206 may be automaticallyformed by social-networking system 160 in response to a particular useraction. As an example and not by way of limitation, if a first useruploads a picture, watches a movie, or listens to a song, an edge 206may be formed between user node 202 corresponding to the first user andconcept nodes 204 corresponding to those concepts. Although thisdisclosure describes forming particular edges 206 in particular manners,this disclosure contemplates forming any suitable edges 206 in anysuitable manner.

Typeahead Processes

In particular embodiments, one or more client-side and/or backend(server-side) processes may implement and utilize a “typeahead” featurethat may automatically attempt to match social-graph elements (e.g.,user nodes 202, concept nodes 204, or edges 206) to informationcurrently being entered by a user in an input form rendered inconjunction with a requested page (such as, for example, a user-profilepage, a concept-profile page, a search-results page, a user interface ofa native application associated with the online social network, oranother suitable page of the online social network), which may be hostedby or accessible in the social-networking system 160. In particularembodiments, as a user is entering text to make a declaration, thetypeahead feature may attempt to match the string of textual charactersbeing entered in the declaration to strings of characters (e.g., names,descriptions) corresponding to user, concepts, or edges and theircorresponding elements in the social graph 200. In particularembodiments, when a match is found, the typeahead feature mayautomatically populate the form with a reference to the social-graphelement (such as, for example, the node name/type, node ID, edgename/type, edge ID, or another suitable reference or identifier) of theexisting social-graph element.

In particular embodiments, as a user types or otherwise enters text intoa form used to add content or make declarations in various sections ofthe user's profile page, home page, or other page, the typeahead processmay work in conjunction with one or more frontend (client-side) and/orbackend (server-side) typeahead processes (hereinafter referred tosimply as “typeahead process”) executing at (or within) thesocial-networking system 160 (e.g., within servers 162), tointeractively and virtually instantaneously (as appearing to the user)attempt to auto-populate the form with a term or terms corresponding tonames of existing social-graph elements, or terms associated withexisting social-graph elements, determined to be the most relevant orbest match to the characters of text entered by the user as the userenters the characters of text. Utilizing the social-graph information ina social-graph database or information extracted and indexed from thesocial-graph database, including information associated with nodes andedges, the typeahead processes, in conjunction with the information fromthe social-graph database, as well as potentially in conjunction withvarious others processes, applications, or databases located within orexecuting within social-networking system 160, may be able to predict auser's intended declaration with a high degree of precision. However,the social-networking system 160 can also provides user's with thefreedom to enter essentially any declaration they wish, enabling usersto express themselves freely.

In particular embodiments, as a user enters text characters into a formbox or other field, the typeahead processes may attempt to identifyexisting social-graph elements (e.g., user nodes 202, concept nodes 204,or edges 206) that match the string of characters entered in the user'sdeclaration as the user is entering the characters. In particularembodiments, as the user enters characters into a form box, thetypeahead process may read the string of entered textual characters. Aseach keystroke is made, the frontend-typeahead process may send theentered character string as a request (or call) to the backend-typeaheadprocess executing within social-networking system 160. In particularembodiments, the typeahead processes may communicate via AJAX(Asynchronous JavaScript and XML) or other suitable techniques, andparticularly, asynchronous techniques. In particular embodiments, therequest may be, or comprise, an XMLHTTPRequest (XHR) enabling quick anddynamic sending and fetching of results. In particular embodiments, thetypeahead process may also send before, after, or with the request asection identifier (section ID) that identifies the particular sectionof the particular page in which the user is making the declaration. Inparticular embodiments, a user ID parameter may also be sent, but thismay be unnecessary in some embodiments, as the user may already be“known” based on the user having logged into (or otherwise beenauthenticated by) the social-networking system 160.

In particular embodiments, the typeahead process may use one or morematching algorithms to attempt to identify matching social-graphelements. In particular embodiments, when a match or matches are found,the typeahead process may send a response (which may utilize AJAX orother suitable techniques) to the user's client system 130 that mayinclude, for example, the names (name strings) or descriptions of thematching social-graph elements as well as, potentially, other metadataassociated with the matching social-graph elements. As an example andnot by way of limitation, if a user entering the characters “pok” into aquery field, the typeahead process may display a drop-down menu thatdisplays names of matching existing profile pages and respective usernodes 202 or concept nodes 204, such as a profile page named or devotedto “poker” or “pokemon”, which the user can then click on or otherwiseselect thereby confirming the desire to declare the matched user orconcept name corresponding to the selected node. As another example andnot by way of limitation, upon clicking “poker,” the typeahead processmay auto-populate, or causes the web browser 132 to auto-populate, thequery field with the declaration “poker”. In particular embodiments, thetypeahead process may simply auto-populate the field with the name orother identifier of the top-ranked match rather than display a drop-downmenu. The user may then confirm the auto-populated declaration simply bykeying “enter” on his or her keyboard or by clicking on theauto-populated declaration.

More information on typeahead processes may be found in U.S. patentapplication Ser. No. 12/763,162, filed 19 Apr. 2010, and U.S. patentapplication Ser. No. 13/556,072, filed 23 Jul. 2012, which areincorporated by reference.

Structured Search Queries

FIG. 3 illustrates an example page of an online social network. Inparticular embodiments, a user may submit a query to thesocial-networking system 160 by inputting text into query field 350. Auser of an online social network may search for information relating toa specific subject matter (e.g., users, concepts, external content orresource) by providing a short phrase describing the subject matter,often referred to as a “search query,” to a search engine. The query maybe an unstructured text query and may comprise one or more text strings(which may include one or more n-grams). In general, a user may inputany character string into query field 350 to search for content on thesocial-networking system 160 that matches the text query. Thesocial-networking system 160 may then search a data store 164 (or, inparticular, a social-graph database) to identify content matching thequery. The search engine may conduct a search based on the query phraseusing various search algorithms and generate search results thatidentify resources or content (e.g., user-profile pages, content-profilepages, or external resources) that are most likely to be related to thesearch query. To conduct a search, a user may input or send a searchquery to the search engine. In response, the search engine may identifyone or more resources that are likely to be related to the search query,each of which may individually be referred to as a “search result,” orcollectively be referred to as the “search results” corresponding to thesearch query. The identified content may include, for example,social-graph elements (i.e., user nodes 202, concept nodes 204, edges206), profile pages, external webpages, or any combination thereof. Thesocial-networking system 160 may then generate a search-results pagewith search results corresponding to the identified content and send thesearch-results page to the user. The search results may be presented tothe user, often in the form of a list of links on the search-resultspage, each link being associated with a different page that containssome of the identified resources or content. In particular embodiments,each link in the search results may be in the form of a Uniform ResourceLocator (URL) that specifies where the corresponding page is located andthe mechanism for retrieving it. The social-networking system 160 maythen send the search-results page to the web browser 132 on the user'sclient system 130. The user may then click on the URL links or otherwiseselect the content from the search-results page to access the contentfrom the social-networking system 160 or from an external system (suchas, for example, a third-party system 170), as appropriate. Theresources may be ranked and presented to the user according to theirrelative degrees of relevance to the search query. The search resultsmay also be ranked and presented to the user according to their relativedegree of relevance to the user. In other words, the search results maybe personalized for the querying user based on, for example,social-graph information, user information, search or browsing historyof the user, or other suitable information related to the user. Inparticular embodiments, ranking of the resources may be determined by aranking algorithm implemented by the search engine. As an example andnot by way of limitation, resources that are more relevant to the searchquery or to the user may be ranked higher than the resources that areless relevant to the search query or the user. In particularembodiments, the search engine may limit its search to resources andcontent on the online social network. However, in particularembodiments, the search engine may also search for resources or contentson other sources, such as a third-party system 170, the internet orWorld Wide Web, or other suitable sources. Although this disclosuredescribes querying the social-networking system 160 in a particularmanner, this disclosure contemplates querying the social-networkingsystem 160 in any suitable manner.

In particular embodiments, the typeahead processes described herein maybe applied to search queries entered by a user. As an example and not byway of limitation, as a user enters text characters into a query field350, a typeahead process may attempt to identify one or more user nodes202, concept nodes 204, or edges 206 that match the string of charactersentered into query field 350 as the user is entering the characters. Asthe typeahead process receives requests or calls including a string orn-gram from the text query, the typeahead process may perform or causesto be performed a search to identify existing social-graph elements(i.e., user nodes 202, concept nodes 204, edges 206) having respectivenames, types, categories, or other identifiers matching the enteredtext. The typeahead process may use one or more matching algorithms toattempt to identify matching nodes or edges. When a match or matches arefound, the typeahead process may send a response to the user's clientsystem 130 that may include, for example, the names (name strings) ofthe matching nodes as well as, potentially, other metadata associatedwith the matching nodes. The typeahead process may then display adrop-down menu 300 that displays names of matching existing profilepages and respective user nodes 202 or concept nodes 204, and displaysnames of matching edges 206 that may connect to the matching user nodes202 or concept nodes 204, which the user can then click on or otherwiseselect thereby confirming the desire to search for the matched user orconcept name corresponding to the selected node, or to search for usersor concepts connected to the matched users or concepts by the matchingedges. Alternatively, the typeahead process may simply auto-populate theform with the name or other identifier of the top-ranked match ratherthan display a drop-down menu 300. The user may then confirm theauto-populated declaration simply by keying “enter” on a keyboard or byclicking on the auto-populated declaration. Upon user confirmation ofthe matching nodes and edges, the typeahead process may send a requestthat informs the social-networking system 160 of the user's confirmationof a query containing the matching social-graph elements. In response tothe request sent, the social-networking system 160 may automatically (oralternately based on an instruction in the request) call or otherwisesearch a social-graph database for the matching social-graph elements,or for social-graph elements connected to the matching social-graphelements as appropriate. Although this disclosure describes applying thetypeahead processes to search queries in a particular manner, thisdisclosure contemplates applying the typeahead processes to searchqueries in any suitable manner.

In connection with search queries and search results, particularembodiments may utilize one or more systems, components, elements,functions, methods, operations, or steps disclosed in U.S. patentapplication Ser. No. 11/503,093, filed 11 Aug. 2006, U.S. patentapplication Ser. No. 12/977,027, filed 22 Dec. 2010, and U.S. patentapplication Ser. No. 12/978,265, filed 23 Dec. 2010, which areincorporated by reference.

Element Detection and Parsing Ambiguous Terms

FIGS. 4A-4B illustrate example queries of the social network. Inparticular embodiments, in response to a text query received from afirst user (i.e., the querying user), the social-networking system 160may parse the text query and identify portions of the text query thatcorrespond to particular social-graph elements. However, in some cases aquery may include one or more terms that are ambiguous, where anambiguous term is a term that may possibly correspond to multiplesocial-graph elements. To parse the ambiguous term, thesocial-networking system 160 may access a social graph 200 and thenparse the text query to identify the social-graph elements thatcorresponded to ambiguous n-grams from the text query. Thesocial-networking system 160 may then generate a set of structuredqueries, where each structured query corresponds to one of the possiblematching social-graph elements. These structured queries may be based onstrings generated by a grammar model, such that they are rendered in anatural-language syntax with references to the relevant social-graphelements. These structured queries may be presented to the queryinguser, who can then select among the structured queries to indicate whichsocial-graph element the querying user intended to reference with theambiguous term. In response to the querying user's selection, thesocial-networking system 160 may then lock the ambiguous term in thequery to the social-graph element selected by the querying user, andthen generate a new set of structured queries based on the selectedsocial-graph element. FIGS. 4A-4B illustrate various example textqueries in query field 350 and various structured queries generated inresponse in drop-down menus 300 (although other suitable graphical userinterfaces are possible). By providing suggested structured queries inresponse to a user's text query, the social-networking system 160 mayprovide a powerful way for users of the online social network to searchfor elements represented in the social graph 200 based on theirsocial-graph attributes and their relation to various social-graphelements. Structured queries may allow a querying user to search forcontent that is connected to particular users or concepts in the socialgraph 200 by particular edge-types. The structured queries may be sentto the first user and displayed in a drop-down menu 300 (via, forexample, a client-side typeahead process), where the first user can thenselect an appropriate query to search for the desired content. Some ofthe advantages of using the structured queries described herein includefinding users of the online social network based upon limitedinformation, bringing together virtual indexes of content from theonline social network based on the relation of that content to varioussocial-graph elements, or finding content related to you and/or yourfriends. Although this disclosure describes and FIGS. 4A-4B illustrategenerating particular structured queries in a particular manner, thisdisclosure contemplates generating any suitable structured queries inany suitable manner.

In particular embodiments, the social-networking system 160 may receivefrom a querying/first user (corresponding to a first user node 202) anunstructured text query. As an example and not by way of limitation, afirst user may want to search for other users who: (1) are first-degreefriends of the first user; and (2) are associated with StanfordUniversity (i.e., the user nodes 202 are connected by an edge 206 to theconcept node 204 corresponding to the school “Stanford”). The first usermay then enter a text query “friends stanford” into query field 350, asillustrated in FIGS. 4A-4B. As the querying user enters this text queryinto query field 350, the social-networking system 160 may providevarious suggested structured queries, as illustrated in drop-down menus300. As used herein, an unstructured text query refers to a simple textstring inputted by a user. The text query may, of course, be structuredwith respect to standard language/grammar rules (e.g. English languagegrammar). However, the text query will ordinarily be unstructured withrespect to social-graph elements. In other words, a simple text querywill not ordinarily include embedded references to particularsocial-graph elements. Thus, as used herein, a structured query refersto a query that contains references to particular social-graph elements,allowing the search engine to search based on the identified elements.Furthermore, the text query may be unstructured with respect to formalquery syntax. In other words, a simple text query will not necessarilybe in the format of a query command that is directly executable by asearch engine (e.g., the text query “friends stanford” could be parsedto form the query command “intersect(school(Stanford University),friends(me)”, or “/search/me/friends/[node ID for StanfordUniversity]/students/ever-past/intersect”, which could be executed as aquery in a social-graph database). Although this disclosure describesreceiving particular queries in a particular manner, this disclosurecontemplates receiving any suitable queries in any suitable manner.

In particular embodiments, social-networking system 160 may parse theunstructured text query (also simply referred to as a search query)received from the first user (i.e., the querying user) to identify oneor more n-grams. In general, an n-gram is a contiguous sequence of nitems from a given sequence of text or speech. The items may becharacters, phonemes, syllables, letters, words, base pairs, prefixes,or other identifiable items from the sequence of text or speech. Then-gram may comprise one or more characters of text (letters, numbers,punctuation, etc.) entered by the querying user. An n-gram of size onecan be referred to as a “unigram,” of size two can be referred to as a“bigram” or “digram,” of size three can be referred to as a “trigram,”and so on. Each n-gram may include one or more parts from the text queryreceived from the querying user. In particular embodiments, each n-grammay comprise a character string (e.g., one or more characters of text)entered by the first user. As an example and not by way of limitation,the social-networking system 160 may parse the text query “friendsstanford” to identify the following n-grams: friends; stanford; friendsstanford. As another example and not by way of limitation, thesocial-networking system 160 may parse the text query “friends in paloalto” to identify the following n-grams: friends; in; palo; alto;friends in; in palo; palo alto; friend in palo; in palo alto; friends inpalo alto. In particular embodiments, each n-gram may comprise acontiguous sequence of n items from the text query. Although thisdisclosure describes parsing particular queries in a particular manner,this disclosure contemplates parsing any suitable queries in anysuitable manner.

In particular embodiments, social-networking system 160 may identify aplurality of nodes or a plurality of edges corresponding to one or moreof the n-grams of a text query. Identifying social-graph elements thatcorrespond to an n-gram may be done in a variety of manners, such as,for example, by determining or calculating, for each n-gram identifiedin the text query, a score that the n-gram corresponds to a social-graphelement. The score may be, for example, a confidence score, aprobability, a quality, a ranking, another suitable type of score, orany combination thereof. As an example and not by way of limitation, thesocial-networking system 160 may determine a probability score (alsoreferred to simply as a “probability”) that the n-gram corresponds to asocial-graph element, such as a user node 202, a concept node 204, or anedge 206 of social graph 200. The probability score may indicate thelevel of similarity or relevance between the n-gram and a particularsocial-graph element. There may be many different ways to calculate theprobability. The present disclosure contemplates any suitable method tocalculate a probability score for an n-gram identified in a searchquery. In particular embodiments, the social-networking system 160 maydetermine a probability, p, that an n-gram corresponds to a particularsocial-graph element. The probability, p, may be calculated as theprobability of corresponding to a particular social-graph element, k,given a particular search query, X. In other words, the probability maybe calculated as p=(k|X). As an example and not by way of limitation, aprobability that an n-gram corresponds to a social-graph element maycalculated as an probability score denoted as p_(i,j,k). The input maybe a text query X=(x₁, x₂, . . . , x_(N)), and a set of classes. Foreach (i:j) and a class k, the social-networking system 160 may computep_(i,j,k)=p(class(x_(i:j))=k|X). As an example and not by way oflimitation, the n-gram “stanford” could be scored with respect to thefollowing social-graph elements as follows: school “StanfordUniversity”=0.7; location “Stanford, Calif.”=0.2; user “AllenStanford”=0.1. In this example, because the n-gram “stanford”corresponds to multiple social-graph elements, it may be considered anambiguous n-gram by the social-networking system 160. In other words,the n-gram is not immediately resolvable to a single social-graphelement based on the parsing algorithm used by the social-networkingsystem 160. In particular embodiments, after identifying an ambiguousn-gram, the social-networking system 160 may highlight that n-gram inthe text query to indicate that it may correspond to multiplesocial-graph elements. As an example and not by way of limitation, asillustrated in FIG. 4B the term “Stanford” in query field 350 has beenhighlighted with a dashed-underline to indicate that it may correspondto multiple social-graph elements, as discussed previously. Althoughthis disclosure describes determining whether n-grams correspond tosocial-graph elements in a particular manner, this disclosurecontemplates determining whether n-grams correspond to social-graphelements in any suitable manner. Moreover, although this disclosuredescribes determining whether an n-gram corresponds to a social-graphelement using a particular type of score, this disclosure contemplatesdetermining whether an n-gram corresponds to a social-graph elementusing any suitable type of score.

More information on element detection and parsing queries may be foundin U.S. patent application Ser. No. 13/556,072, filed 23 Jul. 2012, U.S.patent application Ser. No. 13/731,866, filed 31 Dec. 2012, and U.S.patent application Ser. No. 13/732,101, filed 31 Dec. 2012, each ofwhich is incorporated by reference.

Generating Structured Search Queries

In particular embodiments, the social-networking system 160 may access acontext-free grammar model comprising a plurality of grammars. Eachgrammar of the grammar model may comprise one or more non-terminaltokens (or “non-terminal symbols”) and one or more terminal tokens (or“terminal symbols”/“query tokens”), where particular non-terminal tokensmay be replaced by terminal tokens. A grammar model is a set offormation rules for strings in a formal language. Although thisdisclosure describes accessing particular grammars, this disclosurecontemplates any suitable grammars.

In particular embodiments, the social-networking system 160 may generateone or more strings using one or more grammars. To generate a string inthe language, one begins with a string consisting of only a single startsymbol. The production rules are then applied in any order, until astring that contains neither the start symbol nor designatednon-terminal symbols is produced. In a context-free grammar, theproduction of each non-terminal symbol of the grammar is independent ofwhat is produced by other non-terminal symbols of the grammar. Thenon-terminal symbols may be replaced with terminal symbols (i.e.,terminal tokens or query tokens). Some of the query tokens maycorrespond to identified nodes or identified edges, as describedpreviously. A string generated by the grammar may then be used as thebasis for a structured query containing references to the identifiednodes or identified edges. The string generated by the grammar may berendered in a natural-language syntax, such that a structured querybased on the string is also rendered in natural language. A context-freegrammar is a grammar in which the left-hand side of each production ruleconsists of only a single non-terminal symbol. A probabilisticcontext-free grammar is a tuple (Σ, N, S, P), where the disjoint sets Σand N specify the terminal and non-terminal symbols, respectively, withSεN being the start symbol. P is the set of productions, which take theform Σ→ξ(p), with ΣεN, ξε(Σ∪N)⁺, and p=Pr(Σ→ξ) the probability that Ewill be expanded into the string ξ. The sum of probabilities p over allexpansions of a given non-terminal E must be one. Although thisdisclosure describes generating strings in a particular manner, thisdisclosure contemplates generating strings in any suitable manner.

In particular embodiments, the social-networking system 160 may generateone or more structured queries. The structured queries may be based onthe natural-language strings generated by one or more grammars, asdescribed previously. Each structured query may include references toone or more of the identified nodes or one or more of the identifiededges 206. This type of structured query may allow the social-networkingsystem 160 to more efficiently search for resources and content relatedto the online social network (such as, for example, profile pages) bysearching for content connected to or otherwise related to theidentified user nodes 202 and the identified edges 206. As an exampleand not by way of limitation, in response to the text query, “show mefriends of my girlfriend,” the social-networking system 160 may generatea structured query “Friends of Stephanie,” where “Friends” and“Stephanie” in the structured query are references corresponding toparticular social-graph elements. The reference to “Stephanie” wouldcorrespond to a particular user node 202 (where the social-networkingsystem 160 has parsed the n-gram “my girlfriend” to correspond with auser node 202 for the user “Stephanie”), while the reference to“Friends” would correspond to friend-type edges 206 connecting that usernode 202 to other user nodes 202 (i.e., edges 206 connecting to“Stephanie's” first-degree friends). When executing this structuredquery, the social-networking system 160 may identify one or more usernodes 202 connected by friend-type edges 206 to the user node 202corresponding to “Stephanie”. As another example and not by way oflimitation, in response to the text query, “friends who work atfacebook,” the social-networking system 160 may generate a structuredquery “My friends who work at Facebook,” where “my friends,” “work at,”and “Facebook” in the structured query are references corresponding toparticular social-graph elements as described previously (i.e., afriend-type edge 206, a work-at-type edge 206, and concept node 204corresponding to the company “Facebook”). These structured queries maybe pre-generated and accessed from a cache or generated dynamically inresponse to input from the user. Although this disclosure describesgenerating particular structured queries in a particular manner, thisdisclosure contemplates generating any suitable structured queries inany suitable manner.

In particular embodiments, social-networking system 160 may score thegenerated structured queries. The score may be, for example, aconfidence score, a probability, a quality, a ranking, another suitabletype of score, or any combination thereof. The structured queries may bescored based on a variety of factors, such as, for example, the page ortype of page the user is accessing, user-engagement factors,business-intelligence data, the click-thru rate of particular queries,the conversion-rate of particular queries, user-preferences of thequerying user, the search history of the user, advertising sponsorshipof particular queries, the querying user's social-graph affinity forsocial-graph elements referenced in particular queries, the intent ofthe user, the general or current popularity of particular queries, theusefulness of particular queries, the geographic location of the user,other suitable factors, or any combination thereof. Although thisdisclosure describes ranking structured queries in a particular manner,this disclosure contemplates ranking structured queries in any suitablemanner.

In particular embodiments, social-networking system 160 may send one ormore of the structured queries to the querying user. As an example andnot by way of limitation, after the structured queries are generated,the social-networking system 160 may send one or more of the structuredqueries as a response (which may utilize AJAX or other suitabletechniques) to the user's client system 130 that may include, forexample, the names (name strings) of the referenced social-graphelements, other query limitations (e.g., Boolean operators, etc.), aswell as, potentially, other metadata associated with the referencedsocial-graph elements. The web browser 132 on the querying user's clientsystem 130 may display the sent structured queries in a drop-down menu300, as illustrated in FIGS. 4A-4B. In particular embodiments, the sentqueries may be presented to the querying user in a ranked order, suchas, for example, based on a rank previously determined as describedabove. Structured queries with better rankings may be presented in amore prominent position. Furthermore, in particular embodiments, onlystructured queries above a threshold rank may be sent or displayed tothe querying user. As an example and not by way of limitation, asillustrated in FIGS. 4A-4B, the structured queries may be presented tothe querying user in a drop-down menu 300 where higher ranked structuredqueries may be presented at the top of the menu, with lower rankedstructured queries presented in descending order down the menu. In theexamples illustrated in FIGS. 4A-4B, only the seven highest rankedqueries are sent and displayed to the user. In particular embodiments,one or more references in a structured query may be highlighted (e.g.,outlined, underlined, circled, bolded, italicized, colored, lighted,offset, in caps) in order to indicate its correspondence to a particularsocial-graph element. As an example and not by way of limitation, asillustrated in FIG. 4B, the references to “Stanford University” and“Stanford, Calif.” are highlighted (outlined) in the structured queriesto indicate that it corresponds to a particular concept node 204.Similarly, the references to “Friends”, “like”, “work at”, and “go to”in the structured queries presented in drop-down menu 300 could also behighlighted to indicate that they correspond to particular edges 206.Although this disclosure describes sending particular structured queriesin a particular manner, this disclosure contemplates sending anysuitable structured queries in any suitable manner.

In particular embodiments, social-networking system 160 may receive fromthe querying user a selection of one of the structured queries. Thenodes and edges referenced in the received structured query may bereferred to as the selected nodes and selected edges, respectively. Asan example and not by way of limitation, the web browser 132 on thequerying user's client system 130 may display the sent structuredqueries in a drop-down menu 300, as illustrated in FIGS. 4A-4B, whichthe user may then click on or otherwise select (e.g., by touching thequery on a touchscreen or keying “enter” on his keyboard) to indicatethe particular structured query the user wants the social-networkingsystem 160 to execute. Although this disclosure describes receivingselections of particular structured queries in a particular manner, thisdisclosure contemplates receiving selections of any suitable structuredqueries in any suitable manner.

More information on structured search queries and grammar models may befound in U.S. patent application Ser. No. 13/556,072, filed 23 Jul.2012, U.S. patent application Ser. No. 13/674,695, filed 12 Nov. 2012,and U.S. patent application Ser. No. 13/731,866, filed 31 Dec. 2012,each of which is incorporated by reference.

Generating Personalized Default Queries

In particular embodiments, the social-networking system 160 may generateone or more personalized default structured queries (herein referred toas simply “personalized queries”) for a particular user of the onlinesocial network. In particular embodiments the personalized queries maybe queries that are of interest or relevant to the first user 202. Inparticular embodiments, social-networking system 160 may generatesuggested personalized queries for a user based on one or moreuser-engagement factors. It may be desirable for social-networkingsystem 160 to provide users with personalized queries that are moreinteresting to the user and thus more likely to be engaged with (e.g.,selected or clicked-thru). A variety of factors may be used to increaseuser engagement, such as, for example, business-intelligence,user-preference/history, social-graph affinity, advertising sponsorship,or other suitable factors may be used to determine which suggestedqueries to serve to the user. Social-networking system 160 may calculatewhich personalized queries are more likely to be engaged with by a user,and then send suggested queries to the user that the user is more likelyto engage with. As an example and not by way of limitation,social-networking system 160 may generate a set of possible suggestedqueries, and score sponsored queries more highly than other queries, andthese sponsored queries may then be sent to the querying user. Inparticular embodiments, the social-networking system 160 may generatethe personalized queries based at least in part on informationassociated with the querying user who is querying or friends of thequerying user. In particular embodiments, the social-networking system160 may identify content objects that may be of interest to the firstuser, and then generate personalized queries based at least in part onwhich queries will generate search results corresponding to the contentobject of interest to the querying user. As an example and not by way oflimitation, if some of the first user's friends attend a particularconcert, the social-networking system 160 may suggest a query suggestionor a null state query of “Photos taken at <concert>” or “Photos of yourfriends at <concert>.” The personalized search query suggestions for aparticular user may provide timely and/or localized suggestions that areof interest to the user. Moreover, the personalized search querysuggestions may change frequently and provide interesting search-resultpages, which may keep the querying user more interested and engaged.Although this disclosure describes generating particular personalizedqueries in a particular manner, this disclosure contemplates generatingany suitable personalized queries in any suitable manner.

In particular embodiments, the social-networking system 160 may score afirst set of nodes from social graph 200 based at least in part on oneor more user-engagement factors. Each node in the first set of nodescorresponds to a content object (e.g., a user node 202 or a concept node204) of the online social network. In this way, social-networking 160may identify objects of interest to the querying user. A user-engagementfactor provides a metric for measuring the engagement of a user of theonline social network, such as, for example, by engaging with otherusers, concepts, content, etc. The score may be, for example, aconfidence score, a probability, a quality, a ranking, another suitabletype of score, or any combination thereof. As an example and not by wayof limitation, the score for each node may represent a probability thatthe first user will engage with the node. As another example and not byway of limitation, the score may represent estimate of the degree anddepth of user interaction with particular objects against a clearlydefined set of goals. Scoring based on user-engagement factors may bebased on a variety of factors, such as, for example, the page or type ofpage the user is accessing, business-intelligence data, the searchhistory of the user, the intent of the user, the geographic location ofthe user, the general or current popularity of particular nodes, othersuitable factors, or any combination thereof. Although this disclosuredescribes scoring particular nodes in a particular manner, thisdisclosure contemplates scoring nodes in any suitable manner.

In particular embodiments, social-networking system 160 may score thefirst set of nodes from social graph 200 based at least in part on oneor more user-engagement factors comprises scoring based at least in parton a business-intelligence data. In this context, business-intelligencedata refers to data gathered by social-networking system 160 that helpspredict or identify nodes that users are more likely to engage with, ornodes that promote one or more business goals. The business-intelligencedata may be data related particular to the user, or related to otherusers of the online social network. As an example and not by way oflimitation, social-networking system 160 may use business-intelligencedata relating to the user to identify nodes or node-types that the firstuser is more likely to engage with. As another example and not by way oflimitation, social-networking system 160 may use business-intelligencedata relating to one or more other users of the online social network(e.g., users within a threshold-degree of separation, users withinparticular groups or networks, or all users of the online socialnetwork) to identify nodes or node-types that those users have or arelikely to engage with, and infer the likelihood of the user to engagewith such nodes.

In particular embodiments, social-networking system 160 may score thefirst set of nodes from social graph 200 based at least in part on auser-preference of the querying user. The user-preferences could bespecified by the querying user, other users (e.g., parents or employersof the querying user), system administrators, third-party systems 170,or otherwise determined by social-networking system 160. Theuser-preferences may specify, for example, nodes or node-types the useris interested and not interested in receiving. As an example and not byway of limitation, social-networking system 160 may identify nodes ornode-types preferred by the querying user, as specified by theuser-preference of the user, and calculate higher scores for nodes ornode-types that are specified as being preferred by the user (similarly,nodes or node-types specified as being not preferred may be scored loweror completely excluded/assigned null scores). In other words, particularcontent objects (e.g., photos, pages, etc.), or object-types preferredby the user (e.g., as indicated in a user preferences, by “likes” of thecontent, etc.) may be scored higher than non-preferred content objectsor object-types.

In particular embodiments, social-networking system 160 may score thefirst set of nodes from social graph 200 based at least in part on auser's search history of the online social network. Nodes that referencesocial-graph elements (or types of elements) that the querying user haspreviously accessed (or been accessed by other users that are relevantto the querying user, such as his friends), or are relevant to thesocial-graph elements the querying user has previously accessed, may bemore likely to of interest to the querying user. Thus, these nodes maybe scored more highly. As an example and not by way of limitation, ifquerying user has previously visited the “Stanford University” profilepage but has never visited the “Stanford, Calif.” profile page, whendetermining the score for nodes connected to these concepts, thesocial-networking system 160 may determine that the nodes connected tothe concept node 204 for “Stanford University” (e.g., photos of StanfordUniversity, people who attended Stanford University, etc.) have arelatively high score because the querying user has previously accessedthe concept node 204 for the school. As another example and not by wayof limitation, if the querying user has previously visited or engagednodes of particular node-types, and not visited or engaged other typesof nodes, then when scoring personalized nodes of particular types,social-networking system 160 may score node-types previously visited orengaged by the querying user higher than other node-types.

In particular embodiments, social-networking system 160 may score thefirst set of nodes from social graph 200 based at least in part on asocial-graph affinity of the user node 202 corresponding to the queryinguser with respect to one or more of the nodes referenced in thestructured query. Nodes that are connected to nodes having relativelyhigh social-graph affinity (e.g., a high affinity coefficient) withrespect to the querying user may be more likely to be of interest to thequerying user. Thus, these nodes may be scored more highly. As anexample and not by way of limitation, social-networking system 160 mayscore a node based on the degree of separation (which may be one measureof affinity) between the user node 202 of the querying user and theparticular node. Nodes that are connected to social-graph elements thatare closer in the social graph 200 to the querying user (i.e., fewerdegrees of separation between the element and the querying user's usernode 202) may be scored more highly than nodes that are connected tosocial-graph elements that are further from the user (i.e., more degreesof separation).

In particular embodiments, social-networking system 160 may score thefirst set of nodes from social graph 200 based at least in part on anintent of the querying user. The intent of the user may first bedetermined, and then suggested nodes related to that intent may begenerated and sent to the user. The determined intent(s) may correspondto particular nodes, queries or query-types, and nodes matching thedetermined intent of the user may be scored more highly. Thesocial-networking system 160 may determine the intent of the user basedon a variety of factors, such as, for example, the time of day, theproximity of the user to other users or objects, social-graphinformation, social-graph affinity, the search history of the user,feedback from the user, the geographic location of the user, otherrelevant information about the user, or any combination thereof. Moreinformation on determining the intent of a querying user may be found inU.S. patent application Ser. No. 13/776,469, filed 25 Feb. 2013, whichis incorporated by reference.

In particular embodiments, social-networking system 160 may identify oneor more common nodes, wherein each common node is connected by one ormore edges of the plurality of edges to one or more nodes of the firstset of nodes having a score greater than a threshold score.Social-networking system 160 may then use the identified common nodes togenerate queries that may generate interesting search results(corresponding to highly scoring nodes, and discussed previously) forthe querying user and suggest those queries to the user. In other words,the identified common nodes are social-graph entities that are connectedto the content objects of interested previously identified (e.g.,high-scoring nodes, as described above). As an example and not by way oflimitation, social-networking system 160 may generate the personalizedquery “Friends who live in San Francisco” if the querying user isvisiting San Francisco, has friends who live in San Francisco, and/orthe friends meet certain threshold conditions. This query may begenerated if several of the querying user's friends are scored highlybased on user-engagement factors, and these friends are then identifiedas being connected to concept node 204 for the city of San Francisco(e.g., because they have checked-in in San Francisco, or otherwisedetermined by social-networking system 160 to be connected to SanFrancisco), which is then identified as a common node. The concept node204 for San Francisco is then referenced in the query “Friends who livein San Francisco,” which is generated as a personalized query that willgenerate search results of interest to the user (i.e., the high-scoringfriends). Social-networking system 160 may then generate one or morepersonalized query suggestions, such as, for example, using a predefinedgrammar of the form “Friends who live in <location>” and replacing<location> with a specific city in which many of the user's friendslive. To determine the specific city, the social-networking system 160may identify the first user's 202 friends and the cities in which theylive. As an example and not by way of limitation, the number of friendswho live in each city may be determined and used to select one or morecities. As another example and not by way of limitation, cities in whichmore than ten of the first user's friends live may be used to generatequery suggestions to be displayed in a null state query dropdown. If thefirst user has twelve friends in San Francisco and ten friends in LosAngeles, then two suggestions may be generated. In another example, thepersonalized query suggestions may be ranked according to the number offriends who live in the cities. Therefore, social-networking system 160may generate the personalized queries “Friends who live in SanFrancisco” and “Friends who live in Los Angeles” for this example.

In particular embodiments, social-networking system 160 may generate thepersonalized query “Photos of my friends taken in <location>” byidentifying one or more locations in which the querying user's friendshave taken photos. If the user's friends have recently taken photos inSeattle, then the suggestion “Photos of my friends taken in Seattle” maybe generated. As another example and not by way of limitation,social-networking system 160 may generate the personalized queries basedon topics of posts that have recently been made by the querying userand/or the user's friends. As an example and not by way of limitation,the suggestions “Posts by my friends about <topic>” may be generated fora topic found in posts made by the user's 202 friends. As anotherexample and not by way of limitation, social-networking system 160 maygenerate the personalized queries for globally trending topics. Inanother example and not by way of limitation, suggested personalizequeries may be generated based on where the user's friends 202 worked,where they studied, and so on. In another example and not by way oflimitation, social-networking system 160 may use dates, for example,“Photos of my friends taken before 2005” to generate personalizedqueries. As another example and not by way of limitation, thesearch-result pages generated by these personalized queries may be ofinterest to the querying user. It should be understood that an exampleof what constitutes an interest for the user may be based on highprobable click-thru rate on the search results, high affinity for theresults, business-intelligence data, conversion-rate, or the like. Inparticular embodiments, examples of grammar templates that socialnetworking system 160 may use may be, but are not limited to: “Friendswho live in <location>,” “Photos of friends who live in <location>,”“Photos of my friends taken in <year>,” “Photos of my friends takenbefore <year>,” “Posts by my friends about <topic>,” or the like.Although this disclosure describes generating particular personalizedqueries in a particular manner, this disclosure contemplates generatingany suitable personalized queries in any suitable manner.

FIG. 5 illustrates example personalized queries of the online socialnetwork. For example, FIG. 5A illustrates a set of example queries,including several personalized queries. For example, the personalizedqueries “Posts by my friends about FIFA World Cup” and “My friends whoare playing Online Poker now,” are provided. The references “FIFA WorldCup” and “Online Poker” are references to particular concept nodes 204of the social graph 200 corresponding to the event “FIFA World Cup” andthe game “Online Poker,” respectively. These are provided as examples,and not intended as limitations. Personalized queries can include anyevent or game, that may be interesting to the user, in the grammar style“Posts by my friends about <event>” and “My friends who are play <game>now.” Furthermore, any user node 202 or concept node 204 that may beinteresting to the user, as well as additional grammars can be used foradditional personalized queries. FIG. 5 provides additional examples ofpersonalized queries, including “Photos of my friends at SXSW MusicFestival,” “Music my friends recently listened to on Spotify,” Myfriends who are currently visiting San Francisco,” and “restaurants myfriends have been to in Palo Alto.” Although this disclosure describesgenerating personalized queries for a user in a particular manner, thisdisclosure contemplates generating personalized queries for a user inany suitable manner.

In particular embodiments, social networking system 160 may generate thepersonalized queries dynamically when the user is providing input to thesystem. In one example, social networking system 160 may generate thepersonalized queries when the user presses enter into the search bar, orstarts typing into the search bar, or provides another input to thesocial networking system 160. In particular embodiments, socialnetworking system 160 may pre-generate the personalized queries ratherthan in response to the user's input. As an example and not in the wayof limitation, system 160 may pre-generate the personalized searchqueries by evaluating all the information posted on social networkingsystem 160. This information may comprise information about posts,photos, or the like, such as information about the photos, such aslocations, topics, and the users who made the posts, and identificationof the friends of the user. Social networking system 160 analyzes theinformation and identifies content and then identifies entities orlocations related to the content. The content may be based onuser-engagement scores such as high probable click-thru rate on thesearch results, high affinity for the results, business-intelligencedata, conversion-rate, or the like. In one example, the set ofinteresting content items may be selected from a database (of contentitems) based on a variety of factors, such as engagement of users withthe content or affinity of users for the content. For example, contentitems having engagement scores greater than a threshold may be selectedas the interesting content items. As an example and not by way oflimitation, social networking system 160 may identify entities such asfriends of a particular person who have taken photos at a particularlocation. Social networking system 160 may identify entities that areconnected to the interesting content items by edges in the social graph.As an example and not by way of limitation, assuming that a number ofphotos are liked by users, and the photos are connected to a node thatrepresents the entity “San Francisco,” for each such entity, socialnetworking system 160 may generate one or more suggested personalizedqueries that include the entity, e.g., “Photos of friends who live inSan Francisco.” In this example, the user may then select one of thesuggestions to execute that suggestion as a query, and social networkingsystem 160 may respond by generating a search-results page correspondingto the selected query. As such, in one example, social networking system160 may identify the entities that are connected to the content,evaluate the quality of those entities and the quality/quantity of thecontent, and use that info to score/rank the possible personalizedsearch queries that would generate search-results pages including thatcontent. In one example, the content can be determined based on auser-engagement score.

Social-networking system 160 may identify a page that a user iscurrently viewing or otherwise accessing and then identifying anysocial-graph elements corresponding to that page. The social-graphelements corresponding to a page may be, for example, the nodecorresponding to a user- or concept-profile page, or the nodes/edgesreferenced in a structured query used to generate a particularsearch-results page. Social-networking system 160 may then generate aset of personalized queries for the page based on the identifiedsocial-graph elements for that page. As an example and not by way oflimitation, referencing FIG. 6, when accessing a user-profile page forthe user “Mark”, which corresponds to the user node 202 for “Mark”, someof the personalized queries for that page may include “Photos of Mark atWorld Cup” or “Photos by Mark in Brazil” or “Books Mark reads by R. R.Martin”, as illustrated in drop-down menu 300, where each of thesepersonalized queries includes a reference to the user node 202 of theuser “Mark”. The generated personalized queries may then be sent to theuser and displayed, for example, in a drop-down menu 300. In particularembodiments, the query field 350 may also serve as the title bar for thepage. In other words, the title bar and query field 350 may effectivelybe a unified field on a particular page. The title bar for a page of theonline social network may include a reference to the social-graphelements that correspond to that page. This title bar may also server asa query field 350 for the page. As such, a user accessing that page maythen interact with the title of the page (e.g., by mousing over thetitle, clicking on it, or otherwise interacting with it), to input aquery. In response to a user interacting with the title/query field, thesocial-networking system 160 may then generate a set of personalizedqueries for the page and automatically send and display these queries ina drop-down menu 300 on the page, as illustrated in FIG. 6, where thedrop-down menu 300 is displayed in association with the query field 350.Although this disclosure describes generating personalized queries for apage in a particular manner, this disclosure contemplates generatingpersonalized queries for a page in any suitable manner.

In particular embodiments, social-networking system 160 may identify anode of the social-graph 200 corresponding to a page currently accessedby a user. A user may access any suitable page, such as, for example, auser-profile page, a concept-profile page, a search-results page, ahomepage, a newsfeed page, a notifications page, an email or messagespage, a user interface of a native application associated with theonline social network, or another suitable page of the online socialnetwork. Particular pages of the online social network may correspond toparticular social-graph elements. In particular embodiments, the usermay currently be accessing a profile page of the online social networkcorresponding to a particular user node 202 or concept node 204. Eachuser of the online social network may have a user-profile page thatcorresponds to a user node 202 of the user. As an example and not by wayof limitation, referencing FIG. 6, which illustrates a user-profile pagefor the user “Mark”, this page may correspond to a user node 202 of theuser “Mark”. Similarly, each concept represented in the online socialnetwork may have a concept-profile page that corresponds to a conceptnode 204 representing that concept.

In particular embodiments, social-networking system 160 may generate oneor more personalized queries that each comprise a reference to theidentified node(s) of the page currently accessed by a user. Thesegenerated personalized queries may be considered the personalizedqueries for the page. Each of these personalized queries may alsocomprise references to one or more edges that are connected to theidentified node. These personalized queries are effectively based oninformation about the querying user, and may be further based on thepage currently being accessed by the user. Where the title bar and thequery field 350 field are unified fields, as described previously,social-networking system 160 may essentially use the title of the page(which itself may be considered a reference to one or more social-graphelements) as a template query upon which query modifications are addedto generate the personalized queries. In particular embodiments, if theuser is accessing a search-results page, then the personalized queriesgenerated by social-networking system 160 may comprise references to thesocial-graph elements referenced in the personalized query used togenerate that search-results page. In other words, if a personalizedquery comprising references to one or more nodes and one or more edgesis used to generate a particular search-results page, then thepersonalized queries generated for that page will also include at leastreferences to the one or more nodes and one or more edges of theoriginal personalized query. Thus, the personalized query used togenerate a particular search-results page may be used as the base uponwhich expansions of that initial query may be suggested as personalizedqueries. Although this disclosure describes generating particularpersonalized queries in a particular manner, this disclosurecontemplates any suitable personalized queries in any suitable manner.Moreover, although this disclosure describes generating personalizedqueries for particular types of pages, this disclosure contemplatesgenerating personalized queries for any suitable types of pages.

In particular embodiments, social-networking system 160 may send one ormore of the personalized queries to the querying user for display on thepage currently accessed by the user. These structured queries may besent and displayed as previously described. As an example and not by wayof limitation, the web browser 132 on the querying user's client system130 may display the sent structured queries in a drop-down menu 300 inassociation with a query field 350 of a page, as illustrated in FIG. 6.The personalized queries generated for a particular page may not bedisplayed until the user interacts with the query field 350, such as,for example, by mousing over or clicking on the query field 350, whichmay cause the personalized queries to be sent and displayed in drop-downmenu 300. The personalized queries displayed in drop-down menu 300 mayenable the user accessing the page to selected one of the personalizedqueries, indicating that the selected personalized query should beexecuted by social-networking system 160. Although this disclosuredescribes sending particular personalized queries in a particularmanner, this disclosure contemplates sending any suitable personalizedqueries in any suitable manner.

In particular embodiments, social-networking system 160 may generate oneor more personalized queries in response to a user accessing a page thatdoes not correspond to a particular social-graph element. A user mayaccess a page of the online social network that does not necessarilycorrespond to any particular social-graph element (such as, for example,a newsfeed page, which may not necessarily correspond to any particularnodes or edges of the social graph 200). In this case, the page may beconsidered to be in a “null state” with respect to identifyingsocial-graph elements that correspond to the page. Similarly, for a pagethat does correspond to one or more social-graph elements, the useraccessing that page may place the query field 350 of the page into anull state by, for example, clearing or deleting any title or query thatthat had previously occupied the field. For a null-state page (or aquery field 350 in a null state), social-networking system 160 maygenerate a set of personalized queries for the page based on a varietyof factors, such as, for example, the page or type of page the user isaccessing, user-engagement factors, business-intelligence data, theclick-thru rate of particular queries, the conversion-rate of particularqueries, user-preferences of the querying user, the search history ofthe user, advertising sponsorship of particular queries, the queryinguser's social-graph affinity for social-graph elements referenced inparticular queries, the intent of the user, the general or currentpopularity of particular queries, the usefulness of particular queries,the geographic location of the querying user, other suitable factors, orany combination thereof. In particular embodiments, when the user isaccessing a page that does not correspond to a particular social-graphelement, the social-networking system 160 may access a set ofpersonalized queries corresponding to the page. Each of thesepersonalized queries may comprise references to one or more edges 206(or edge-types) or one or more nodes (or node-types). As an example andnot by way of limitation, FIG. 3 illustrates a newsfeed page beingaccessed by a user of the online social network. Some of thepersonalized queries for this page may include “Movies my friends like”or “games my friends play”, as illustrated in drop-down menu 300, wherethese structured queries included references to friend-type edges 206and like-type or play-type edges 206, respectively. As another exampleand not by way of limitation, for the same newsfeed page illustrated inFIG. 3, the social-networking system 160 may generate personalizedqueries that include “My friends”, “Photos of my friends”, “Photos Ilike”, or “Apps my friends use”, where these queries include referenceto both edges and nodes (e.g., for the structured query “My friends”,the term “My” is a reference to the user node 202 of the querying userand the term “friends” is a reference to friend-type edges 206 connectedto that node). Although this disclosure describes generatingpersonalized queries for a page that does not correspond to particularsocial-graph elements in a particular manner, this disclosurecontemplates generating personalized queries for a page that does notcorrespond to particular social-graph elements in any suitable manner.

In particular embodiments, social-networking system 160 may send one ormore personalized queries to the querying user. The personalized queriesmay be displayed on a page currently accessed by the first user. As anexample and not by way of limitation, the page may be a profile page ofthe online social network (e.g., a user-profile page of the queryinguser or another user, or a concept-profile page of a concept associatedwith the online social network), a newsfeed page of the online socialnetwork, a search-results page corresponding to a particular structuredquery, or another suitable page of the online social network.

In particular embodiments, social networking system 160 may display athreshold number of personalized search queries to the user. Inparticular embodiments, each sent personalized query may have a scoregreater than a threshold score to the querying user. After scoring thepersonalized queries, the social-networking system 160 may then sendonly those personalized queries having a score greater than a thresholdscore. In some embodiments the social-networking system can display alimited number of personalized queries, for example social networkingsystem 160 may display the top seven personalized search queries to theuser. In another example, social networking system 160 may display anynumber of personalized search queries to the user, without an upper orlower limit to the number of personalized search queries to present tothe user. The quality threshold may be based on, for example,user-engagement factors. In another embodiment, social networking system160 may use a quality threshold to determine which personalized searchqueries to present to the user. Furthermore, social networking system160 may rank the personalized search queries. The ranking may be based,for example, on the score, as described above, or quality of the searchresults corresponding to the query. For example, a photos query thatbrings up photos that have been liked/viewed/commented on a lot may beranked higher than a query that brings up photos that have not beeninteracted with as much. In another example, the rank may be based onthe location of the friends of the user.

In particular embodiments, social networking system 160 may generate thepersonalized query in a typeahead context by still using pre-generatedsuggested queries. In one example, when the user clicks on a query fieldand inputs a text string, the social networking system 160 may providepersonalized queries. In this example, pre-generated suggested queriescorresponding to particular typeahead inputs (e.g., “frien . . . ”,“phot . . . ”, “pag . . . ”) may be cached so they may be retrieved fromthe cache when needed, e.g., to generate the typeahead list when theuser begins typing, and displayed. As such, once the first user 202begins typing the query, social networking system 160 has generated thepersonalized queries for display.

In particular embodiments, social networking system 160 may cache theresults using a storage memory, such as a memcache. In particularembodiments, the social networking system 160 may retrieve thepersonalized queries from the cache when, for example, the user clickson a query field or provides another input to social networking system160. Each search-results page may be tied to a correspondingpersonalized query that may be suggested in, for example, a null state.Furthermore, social networking system 160 may generate a table ofsuggestions and index it by the identification of the user. As such, inone example, if the user clicks on a query field, a request may be sentto social networking system 160, which may load the null state queriesinto a storage memory. In one example, the personalized queries may bepresented to the user as a suggested null state query such as a dropdown below the query field.

FIG. 7 illustrates an example method 700 for generating personalizedqueries for a user. The method can begin at step 710, where thesocial-networking system 160 can access a social graph 200 comprising aplurality of nodes and a plurality of edges 206 connecting the nodes.The nodes can comprise a first user node 202 and a plurality of secondnodes (one or more user nodes 202, concept nodes 204, or any combinationthereof). At step 720, the social networking system 160 can score afirst set of nodes based on engagement factors. The first set of nodescan be one or more of the second nodes. In some embodiments, the method700 can also include determining a first set of content corresponding tothe first set of nodes. The content can be determined based on one ormore user-engagement scores. At step 730, the social networking system160 can identify common nodes. Each common node can be connected by oneor more edges of the plurality of edges to one or more nodes from thefirst set of nodes. The social networking system 160 can identify thecommon nodes based on the nodes from the first set of nodes having ascore greater than a threshold score. In some embodiments, the method700 can also further include determining whether each of the first setof nodes is added to the social graph within a threshold period of time.The threshold period of time can be specified by the user or specifiedby the online social networking system. At step 740, the socialnetworking system 160 can generate structured queries that reference oneor more nodes of the plurality of nodes and one or more edges of theplurality of edges. At least one structured query can be a personalizedquery that references the common node and one or more edges connectingthe common node. At step 750, the social networking system 160 can sendthe generated personalized query to the first user 202 for display. Insome embodiments, the first set of nodes can include one or more ofposts, photos, shares, check-ins, comments, or the like. Particularembodiments can repeat one or more steps of the method of FIG. 7, whereappropriate. Although this disclosure describes and illustratesparticular steps of the method of FIG. 7 as occurring in a particularorder, this disclosure contemplates any suitable steps of the method ofFIG. 7 occurring in any suitable order. Moreover, although thisdisclosure describes and illustrates an example method for generatingpersonalized default queries for a user including the particular stepsof the method of FIG. 7, this disclosure contemplates any suitablemethod for generating personalized default queries for a user includingany suitable steps, which may include all, some, or none of the steps ofthe method of FIG. 7, where appropriate. Furthermore, although thisdisclosure describes and illustrates particular components, devices, orsystems carrying out particular steps of the method of FIG. 7, thisdisclosure contemplates any suitable combination of any suitablecomponents, devices, or systems carrying out any suitable steps of themethod of FIG. 7.

FIG. 8 illustrates an example system for generating personalized queriesfor a user. The social-networking system 160 may receive personalizedqueries from the custom suggestions entity 810 and suggestions from thedefault suggestions entity 840. In some embodiments, the personalizedgrammar suggestions can be generated using a builder entity 820 and avalidator entity 830. The builder 820 entity may generate customsuggestions and the validator entity may validate the custom suggestionsand review the search-results resulting from the custom suggestions. Thesocial-networking system 160 may then merge 850 those suggestions withdefault suggestion 840. The social-networking system 160 may add orreplace suggestions received from the default suggestion entity withpersonalized query received from the custom suggestion entity 810. Thesocial-networking system 160 may then reorder 860 the suggestions. Thereordering 860 of the suggestion may be based on the rank that thesystem gives the suggestions. The social-networking system 160 may thenprovide the final null state 870 or the personalized queries. It shouldbe understood that the personalized queries may be used in all graphsearch queries such as null state queries, typeahead search queries, orthe like.

Generating Search Results

In particular embodiments, in response to a structured query receivedfrom a querying user, the social-networking system 160 may generate oneor more search results, where each search result matches (orsubstantially matches) the terms of the structured query (which includessponsored queries, dynamic queries, and other types of structuredqueries). The social-networking system 160 may receive a structuredquery from a querying user (also referred to as the “first user”,corresponding to a first user node 202). In response to the structuredquery, the social-networking system 160 may generate one or more searchresults corresponding to the structured query. Each search result mayinclude link to a profile page and a description or summary of theprofile page (or the node corresponding to that page). The searchresults may be presented and sent to the querying user as asearch-results page. In the case of a personalized query, the searchresults may contain references one or more objects identified as beingof interest to the querying user (e.g., based on user-engagementfactors, etc.). The structured query used to generate a particularsearch-results page may be shown in query field 350, and the varioussearch results generated in response to the structured query areillustrated in a results field for presented search results. Inparticular embodiments, the query field 350 may also serve as the titlebar for the page. In other words, the title bar and query field 350 mayeffectively be a unified field on the search-results page. Thesearch-results page may also include a field for modifying searchresults and a field for sharing structured queries and search results.When generating the search results, the social-networking system 160 maygenerate one or more snippets for each search result, where the snippetsare contextual information about the target of the search result (i.e.,contextual information about the social-graph entity, profile page, orother content corresponding to the particular search result). Althoughthis disclosure describes and illustrates particular search-resultspages, this disclosure contemplates any suitable search-results pages.

In particular embodiments, the social-networking system 160 may generateone or more search results corresponding to a structured query. Thesearch results may identify resources or content (e.g., user-profilepages, content-profile pages, or external resources) that match or arelikely to be related to the search query. In particular embodiments,each search result may correspond to a particular user node 202 orconcept node 204 of the social graph 200. The search result may includea link to the profile page associated with the node, as well ascontextual information about the node (i.e., contextual informationabout the user or concept that corresponds to the node). Each searchresult in results field shows a link to a profile page of a user(illustrated as the user's name, which contains an inline link to theprofile page) and contextual information about that user thatcorresponds to a user node 202 of the social graph 200. Each searchresult may also show a thumbnail of a photograph that corresponds to aconcept node 204 of the social graph. In particular embodiments, eachsearch result may correspond to a node that is connected to one or moreof the selected nodes by one or more of the selected edges of thestructured query. In particular embodiments, the social-networkingsystem 160 may also transmit advertisements or other sponsored contentto the client system 130 in response to the structured query. Theadvertisements may be included in as part of the search results, orseparately. The advertisements may correspond to one or more of theobjects referenced in the search results. In particular embodiments, thesocial-networking system 160 may filter out one or more search resultsidentifying particular resources or content based on the privacysettings associated with the users associated with those resources orcontent. Although this disclosure describes generating particular searchresults in a particular manner, this disclosure contemplates generatingany suitable search results in any suitable manner.

More information on generating search results may be found in U.S.patent application Ser. No. 13/556,017, filed 23 Jul. 2012, and U.S.patent application Ser. No. 13/731,939, filed 31 Dec. 2012, each ofwhich is incorporated by reference.

Social Graph Affinity and Coefficient

In particular embodiments, social-networking system 160 may determinethe social-graph affinity (which may be referred to herein as“affinity”) of various social-graph entities for each other. Affinitymay represent the strength of a relationship or level of interestbetween particular objects associated with the online social network,such as users, concepts, content, actions, advertisements, other objectsassociated with the online social network, or any suitable combinationthereof. Affinity may also be determined with respect to objectsassociated with third-party systems 170 or other suitable systems. Anoverall affinity for a social-graph entity for each user, subjectmatter, or type of content may be established. The overall affinity maychange based on continued monitoring of the actions or relationshipsassociated with the social-graph entity. Although this disclosuredescribes determining particular affinities in a particular manner, thisdisclosure contemplates determining any suitable affinities in anysuitable manner.

In particular embodiments, social-networking system 160 may measure orquantify social-graph affinity using an affinity coefficient (which maybe referred to herein as “coefficient”). The coefficient may representor quantify the strength of a relationship between particular objectsassociated with the online social network. The coefficient may alsorepresent a probability or function that measures a predictedprobability that a user will perform a particular action based on theuser's interest in the action. In this way, a user's future actions maybe predicted based on the user's prior actions, where the coefficientmay be calculated at least in part a the history of the user's actions.Coefficients may be used to predict any number of actions, which may bewithin or outside of the online social network. As an example and not byway of limitation, these actions may include various types ofcommunications, such as sending messages, posting content, or commentingon content; various types of a observation actions, such as accessing orviewing profile pages, media, or other suitable content; various typesof coincidence information about two or more social-graph entities, suchas being in the same group, tagged in the same photograph, checked-in atthe same location, or attending the same event; or other suitableactions. Although this disclosure describes measuring affinity in aparticular manner, this disclosure contemplates measuring affinity inany suitable manner.

In particular embodiments, social-networking system 160 may use avariety of factors to calculate a coefficient. These factors mayinclude, for example, user actions, types of relationships betweenobjects, location information, other suitable factors, or anycombination thereof. In particular embodiments, different factors may beweighted differently when calculating the coefficient. The weights foreach factor may be static or the weights may change according to, forexample, the user, the type of relationship, the type of action, theuser's location, and so forth. Ratings for the factors may be combinedaccording to their weights to determine an overall coefficient for theuser. As an example and not by way of limitation, particular useractions may be assigned both a rating and a weight while a relationshipassociated with the particular user action is assigned a rating and acorrelating weight (e.g., so the weights total 100%). To calculate thecoefficient of a user towards a particular object, the rating assignedto the user's actions may comprise, for example, 60% of the overallcoefficient, while the relationship between the user and the object maycomprise 40% of the overall coefficient. In particular embodiments, thesocial-networking system 160 may consider a variety of variables whendetermining weights for various factors used to calculate a coefficient,such as, for example, the time since information was accessed, decayfactors, frequency of access, relationship to information orrelationship to the object about which information was accessed,relationship to social-graph entities connected to the object, short- orlong-term averages of user actions, user feedback, other suitablevariables, or any combination thereof. As an example and not by way oflimitation, a coefficient may include a decay factor that causes thestrength of the signal provided by particular actions to decay withtime, such that more recent actions are more relevant when calculatingthe coefficient. The ratings and weights may be continuously updatedbased on continued tracking of the actions upon which the coefficient isbased. Any type of process or algorithm may be employed for assigning,combining, averaging, and so forth the ratings for each factor and theweights assigned to the factors. In particular embodiments,social-networking system 160 may determine coefficients usingmachine-learning algorithms trained on historical actions and past userresponses, or data farmed from users by exposing them to various optionsand measuring responses. Although this disclosure describes calculatingcoefficients in a particular manner, this disclosure contemplatescalculating coefficients in any suitable manner.

In particular embodiments, social-networking system 160 may calculate acoefficient based on a user's actions. Social-networking system 160 maymonitor such actions on the online social network, on a third-partysystem 170, on other suitable systems, or any combination thereof. Anysuitable type of user actions may be tracked or monitored. Typical useractions include viewing profile pages, creating or posting content,interacting with content, tagging or being tagged in images, joininggroups, listing and confirming attendance at events, checking-in atlocations, liking particular pages, creating pages, and performing othertasks that facilitate social action. In particular embodiments,social-networking system 160 may calculate a coefficient based on theuser's actions with particular types of content. The content may beassociated with the online social network, a third-party system 170, oranother suitable system. The content may include users, profile pages,posts, news stories, headlines, instant messages, chat roomconversations, emails, advertisements, pictures, video, music, othersuitable objects, or any combination thereof. Social-networking system160 may analyze a user's actions to determine whether one or more of theactions indicate an affinity for subject matter, content, other users,and so forth. As an example and not by way of limitation, if a user maymake frequently posts content related to “coffee” or variants thereof,social-networking system 160 may determine the user has a highcoefficient with respect to the concept “coffee”. Particular actions ortypes of actions may be assigned a higher weight and/or rating thanother actions, which may affect the overall calculated coefficient. Asan example and not by way of limitation, if a first user emails a seconduser, the weight or the rating for the action may be higher than if thefirst user simply views the user-profile page for the second user.

In particular embodiments, social-networking system 160 may calculate acoefficient based on the type of relationship between particularobjects. Referencing the social graph 200, social-networking system 160may analyze the number and/or type of edges 206 connecting particularuser nodes 202 and concept nodes 204 when calculating a coefficient. Asan example and not by way of limitation, user nodes 202 that areconnected by a spouse-type edge (representing that the two users aremarried) may be assigned a higher coefficient than a user nodes 202 thatare connected by a friend-type edge. In other words, depending upon theweights assigned to the actions and relationships for the particularuser, the overall affinity may be determined to be higher for contentabout the user's spouse than for content about the user's friend. Inparticular embodiments, the relationships a user has with another objectmay affect the weights and/or the ratings of the user's actions withrespect to calculating the coefficient for that object. As an exampleand not by way of limitation, if a user is tagged in first photo, butmerely likes a second photo, social-networking system 160 may determinethat the user has a higher coefficient with respect to the first photothan the second photo because having a tagged-in-type relationship withcontent may be assigned a higher weight and/or rating than having alike-type relationship with content. In particular embodiments,social-networking system 160 may calculate a coefficient for a firstuser based on the relationship one or more second users have with aparticular object. In other words, the connections and coefficientsother users have with an object may affect the first user's coefficientfor the object. As an example and not by way of limitation, if a firstuser is connected to or has a high coefficient for one or more secondusers, and those second users are connected to or have a highcoefficient for a particular object, social-networking system 160 maydetermine that the first user should also have a relatively highcoefficient for the particular object. In particular embodiments, thecoefficient may be based on the degree of separation between particularobjects. The lower coefficient may represent the decreasing likelihoodthat the first user will share an interest in content objects of theuser that is indirectly connected to the first user in the social graph200. As an example and not by way of limitation, social-graph entitiesthat are closer in the social graph 200 (i.e., fewer degrees ofseparation) may have a higher coefficient than entities that are furtherapart in the social graph 200.

In particular embodiments, social-networking system 160 may calculate acoefficient based on location information. Objects that aregeographically closer to each other may be considered to be more relatedor of more interest to each other than more distant objects. Inparticular embodiments, the coefficient of a user towards a particularobject may be based on the proximity of the object's location to acurrent location associated with the user (or the location of a clientsystem 130 of the user). A first user may be more interested in otherusers or concepts that are closer to the first user. As an example andnot by way of limitation, if a user is one mile from an airport and twomiles from a gas station, social-networking system 160 may determinethat the user has a higher coefficient for the airport than the gasstation based on the proximity of the airport to the user.

In particular embodiments, social-networking system 160 may performparticular actions with respect to a user based on coefficientinformation. Coefficients may be used to predict whether a user willperform a particular action based on the user's interest in the action.A coefficient may be used when generating or presenting any type ofobjects to a user, such as advertisements, search results, news stories,media, messages, notifications, or other suitable objects. Thecoefficient may also be utilized to rank and order such objects, asappropriate. In this way, social-networking system 160 may provideinformation that is relevant to user's interests and currentcircumstances, increasing the likelihood that they will find suchinformation of interest. In particular embodiments, social-networkingsystem 160 may generate content based on coefficient information.Content objects may be provided or selected based on coefficientsspecific to a user. As an example and not by way of limitation, thecoefficient may be used to generate media for the user, where the usermay be presented with media for which the user has a high overallcoefficient with respect to the media object. As another example and notby way of limitation, the coefficient may be used to generateadvertisements for the user, where the user may be presented withadvertisements for which the user has a high overall coefficient withrespect to the advertised object. In particular embodiments,social-networking system 160 may generate search results based oncoefficient information. Search results for a particular user may bescored or ranked based on the coefficient associated with the searchresults with respect to the querying user. As an example and not by wayof limitation, search results corresponding to objects with highercoefficients may be ranked higher on a search-results page than resultscorresponding to objects having lower coefficients.

In particular embodiments, social-networking system 160 may calculate acoefficient in response to a request for a coefficient from a particularsystem or process. To predict the likely actions a user may take (or maybe the subject of) in a given situation, any process may request acalculated coefficient for a user. The request may also include a set ofweights to use for various factors used to calculate the coefficient.This request may come from a process running on the online socialnetwork, from a third-party system 170 (e.g., via an API or othercommunication channel), or from another suitable system. In response tothe request, social-networking system 160 may calculate the coefficient(or access the coefficient information if it has previously beencalculated and stored). In particular embodiments, social-networkingsystem 160 may measure an affinity with respect to a particular process.Different processes (both internal and external to the online socialnetwork) may request a coefficient for a particular object or set ofobjects. Social-networking system 160 may provide a measure of affinitythat is relevant to the particular process that requested the measure ofaffinity. In this way, each process receives a measure of affinity thatis tailored for the different context in which the process will use themeasure of affinity.

In connection with social-graph affinity and affinity coefficients,particular embodiments may utilize one or more systems, components,elements, functions, methods, operations, or steps disclosed in U.S.patent application Ser. No. 11/503,093, filed 11 Aug. 2006, U.S. patentapplication Ser. No. 12/977,027, filed 22 Dec. 2010, U.S. patentapplication Ser. No. 12/978,265, filed 23 Dec. 2010, and U.S. patentapplication Ser. No. 13/632,869, field 1 Oct. 2012, each of which isincorporated by reference.

Systems and Methods

FIG. 9 illustrates an example computer system 900. In particularembodiments, one or more computer systems 900 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 900 provide functionalitydescribed or illustrated herein. In particular embodiments, softwarerunning on one or more computer systems 900 performs one or more stepsof one or more methods described or illustrated herein or providesfunctionality described or illustrated herein. Particular embodimentsinclude one or more portions of one or more computer systems 900.Herein, reference to a computer system may encompass a computing device,and vice versa, where appropriate. Moreover, reference to a computersystem may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems900. This disclosure contemplates computer system 900 taking anysuitable physical form. As example and not by way of limitation,computer system 900 may be an embedded computer system, a system-on-chip(SOC), a single-board computer system (SBC) (such as, for example, acomputer-on-module (COM) or system-on-module (SOM)), a desktop computersystem, a laptop or notebook computer system, an interactive kiosk, amainframe, a mesh of computer systems, a mobile telephone, a personaldigital assistant (PDA), a server, a tablet computer system, or acombination of two or more of these. Where appropriate, computer system900 may include one or more computer systems 900; be unitary ordistributed; span multiple locations; span multiple machines; spanmultiple data centers; or reside in a cloud, which may include one ormore cloud components in one or more networks. Where appropriate, one ormore computer systems 900 may perform without substantial spatial ortemporal limitation one or more steps of one or more methods describedor illustrated herein. As an example and not by way of limitation, oneor more computer systems 900 may perform in real time or in batch modeone or more steps of one or more methods described or illustratedherein. One or more computer systems 900 may perform at different timesor at different locations one or more steps of one or more methodsdescribed or illustrated herein, where appropriate.

In particular embodiments, computer system 900 includes a processor 902,memory 904, storage 906, an input/output (I/O) interface 908, acommunication interface 910, and a bus 912. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 902 includes hardware for executinginstructions, such as those making up a computer program. As an exampleand not by way of limitation, to execute instructions, processor 902 mayretrieve (or fetch) the instructions from an internal register, aninternal cache, memory 904, or storage 906; decode and execute them; andthen write one or more results to an internal register, an internalcache, memory 904, or storage 906. In particular embodiments, processor902 may include one or more internal caches for data, instructions, oraddresses. This disclosure contemplates processor 902 including anysuitable number of any suitable internal caches, where appropriate. Asan example and not by way of limitation, processor 902 may include oneor more instruction caches, one or more data caches, and one or moretranslation lookaside buffers (TLBs). Instructions in the instructioncaches may be copies of instructions in memory 904 or storage 906, andthe instruction caches may speed up retrieval of those instructions byprocessor 902. Data in the data caches may be copies of data in memory904 or storage 906 for instructions executing at processor 902 tooperate on; the results of previous instructions executed at processor902 for access by subsequent instructions executing at processor 902 orfor writing to memory 904 or storage 906; or other suitable data. Thedata caches may speed up read or write operations by processor 902. TheTLBs may speed up virtual-address translation for processor 902. Inparticular embodiments, processor 902 may include one or more internalregisters for data, instructions, or addresses. This disclosurecontemplates processor 902 including any suitable number of any suitableinternal registers, where appropriate. Where appropriate, processor 902may include one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 902. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 904 includes main memory for storinginstructions for processor 902 to execute or data for processor 902 tooperate on. As an example and not by way of limitation, computer system900 may load instructions from storage 906 or another source (such as,for example, another computer system 900) to memory 904. Processor 902may then load the instructions from memory 904 to an internal registeror internal cache. To execute the instructions, processor 902 mayretrieve the instructions from the internal register or internal cacheand decode them. During or after execution of the instructions,processor 902 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor902 may then write one or more of those results to memory 904. Inparticular embodiments, processor 902 executes only instructions in oneor more internal registers or internal caches or in memory 904 (asopposed to storage 906 or elsewhere) and operates only on data in one ormore internal registers or internal caches or in memory 904 (as opposedto storage 906 or elsewhere). One or more memory buses (which may eachinclude an address bus and a data bus) may couple processor 902 tomemory 904. Bus 912 may include one or more memory buses, as describedbelow. In particular embodiments, one or more memory management units(MMUs) reside between processor 902 and memory 904 and facilitateaccesses to memory 904 requested by processor 902. In particularembodiments, memory 904 includes random access memory (RAM). This RAMmay be volatile memory, where appropriate. Where appropriate, this RAMmay be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 904 may include one ormore memories 904, where appropriate. Although this disclosure describesand illustrates particular memory, this disclosure contemplates anysuitable memory.

In particular embodiments, storage 906 includes mass storage for data orinstructions. As an example and not by way of limitation, storage 906may include a hard disk drive (HDD), a floppy disk drive, flash memory,an optical disc, a magneto-optical disc, magnetic tape, or a UniversalSerial Bus (USB) drive or a combination of two or more of these. Storage906 may include removable or non-removable (or fixed) media, whereappropriate. Storage 906 may be internal or external to computer system900, where appropriate. In particular embodiments, storage 906 isnon-volatile, solid-state memory. In particular embodiments, storage 906includes read-only memory (ROM). Where appropriate, this ROM may bemask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM),electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM),or flash memory or a combination of two or more of these. Thisdisclosure contemplates mass storage 906 taking any suitable physicalform. Storage 906 may include one or more storage control unitsfacilitating communication between processor 902 and storage 906, whereappropriate. Where appropriate, storage 906 may include one or morestorages 906. Although this disclosure describes and illustratesparticular storage, this disclosure contemplates any suitable storage.

In particular embodiments, I/O interface 908 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 900 and one or more I/O devices. Computer system900 may include one or more of these I/O devices, where appropriate. Oneor more of these I/O devices may enable communication between a personand computer system 900. As an example and not by way of limitation, anI/O device may include a keyboard, keypad, microphone, monitor, mouse,printer, scanner, speaker, still camera, stylus, tablet, touch screen,trackball, video camera, another suitable I/O device or a combination oftwo or more of these. An I/O device may include one or more sensors.This disclosure contemplates any suitable I/O devices and any suitableI/O interfaces 908 for them. Where appropriate, I/O interface 908 mayinclude one or more device or software drivers enabling processor 902 todrive one or more of these I/O devices. I/O interface 908 may includeone or more I/O interfaces 908, where appropriate. Although thisdisclosure describes and illustrates a particular I/O interface, thisdisclosure contemplates any suitable I/O interface.

In particular embodiments, communication interface 910 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 900 and one or more other computer systems 900 or one ormore networks. As an example and not by way of limitation, communicationinterface 910 may include a network interface controller (NIC) ornetwork adapter for communicating with an Ethernet or other wire-basednetwork or a wireless NIC (WNIC) or wireless adapter for communicatingwith a wireless network, such as a WI-FI network. This disclosurecontemplates any suitable network and any suitable communicationinterface 910 for it. As an example and not by way of limitation,computer system 900 may communicate with an ad hoc network, a personalarea network (PAN), a local area network (LAN), a wide area network(WAN), a metropolitan area network (MAN), or one or more portions of theInternet or a combination of two or more of these. One or more portionsof one or more of these networks may be wired or wireless. As anexample, computer system 900 may communicate with a wireless PAN (WPAN)(such as, for example, a BLUETOOTH WPAN), a WI-FI network, a WI-MAXnetwork, a cellular telephone network (such as, for example, a GlobalSystem for Mobile Communications (GSM) network), or other suitablewireless network or a combination of two or more of these. Computersystem 900 may include any suitable communication interface 910 for anyof these networks, where appropriate. Communication interface 910 mayinclude one or more communication interfaces 910, where appropriate.Although this disclosure describes and illustrates a particularcommunication interface, this disclosure contemplates any suitablecommunication interface.

In particular embodiments, bus 912 includes hardware, software, or bothcoupling components of computer system 900 to each other. As an exampleand not by way of limitation, bus 912 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCIe) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 912may include one or more buses 912, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Miscellaneous

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative.

What is claimed is:
 1. A method comprising, by one or more computingsystems: scoring a first set of content objects of a plurality ofcontent objects of an online social network based on one or moreuser-engagement factors; identifying one or more related contentobjects, wherein each related content objects is connected within theonline social network to one or more content objects of the first set ofcontent objects having a score greater than a threshold score;generating a plurality of structured queries that each comprisereferences to one or more content objects of the plurality of contentobjects, wherein at least one of the structured queries is apersonalized query comprising a reference to at least one of the relatedcontent objects; and sending, to a client device of a first user,instructions for presenting one or more of the generated structuredqueries to the first user for display on an interface currently accessedby the first user, wherein at least one of the sent structured queriesis a personalized query.
 2. The method of claim 1, wherein the first setof content objects comprises one or more of: posts, photos, shares,check-ins, comments, or any combination thereof.
 3. The method of claim2, further comprising determining whether each of the first set ofcontent objects is added to the online social network within a thresholdperiod of time, wherein the threshold period of time is specified by thefirst user.
 4. The method of claim 2, further comprising determiningwhether each of the first set of content objects is added to the onlinesocial network within a threshold period of time, wherein the thresholdperiod of time is specified by the online social networking system. 5.The method of claim 1, wherein scoring the first set of content objectsof the plurality of content objects based on one or more user-engagementfactors comprises scoring based at least in part on abusiness-intelligence data.
 6. The method of claim 1, wherein scoringthe first set of content objects of the plurality of content objectsbased on one or more user-engagement factors comprises scoring the firstset of content objects based at least in part on a click-thru rate forthe one or more generated structured queries.
 7. The method of claim 1,wherein scoring the first set of content objects of the plurality ofcontent objects based on one or more user-engagement factors comprisesscoring the first set of content objects based at least in part on aconversion-rate for the one or more generated structured queries.
 8. Themethod of claim 1, wherein scoring the first set of content objects ofthe plurality of content objects based on one or more user-engagementfactors comprises scoring based at least in part on a user-preference ofthe first user.
 9. The method of claim 1, further comprising determininga first set of content objects to score.
 10. The method of claim 9,wherein the first set of content objects is determined based on one ormore user-engagement scores.
 11. The method of claim 1, furthercomprising ranking each of the generated structured queries.
 12. Themethod of claim 11, wherein the rank of each of the one or moregenerated structured queries is based on the location of one or morefriends of the first user.
 13. The method of claim 1, further comprisingdisplaying a threshold number of the one or more generated structuredqueries to the first user.
 14. The method of claim 1, further comprisingreceiving an input from the first user, wherein scoring the first set ofcontent objects of the plurality of content objects is further based onthe received input from the first user, and wherein the plurality ofstructured queries are generated in response to the received input fromthe first user.
 15. The method of claim 14, wherein the input from thefirst user is an unstructured text query inputted by the first user. 16.The method of claim 1, wherein the plurality of structured queries arepre-generated rather than generated in response to an action from thefirst user
 17. The method of claim 1, further comprising displaying, atthe client device, one or more of the sent structured queries to thefirst user.
 18. The method of claim 17, wherein the plurality ofstructured queries are cached.
 19. The method of claim 1, furthercomprising: accessing a social graph comprising a plurality of nodes anda plurality of edges connecting the nodes, each of the edges between twoof the nodes representing a single degree of separation between them,the nodes comprising: a first node corresponding to the first user; anda plurality of second nodes corresponding to the plurality of contentobjects of the online social network, respectively.
 20. One or morecomputer-readable non-transitory storage media embodying software thatis operable when executed to: score a first set of content objects of aplurality of content objects of an online social network based on one ormore user-engagement factors; identify one or more related contentobjects, wherein each related content objects is connected within theonline social network to one or more content objects of the first set ofcontent objects having a score greater than a threshold score; generatea plurality of structured queries that each comprise references to oneor more content objects of the plurality of content objects, wherein atleast one of the structured queries is a personalized query comprising areference to at least one of the related content objects; and send, to aclient device of a first user, instructions for presenting one or moreof the generated structured queries to the first user for display on aninterface currently accessed by the first user, wherein at least one ofthe sent structured queries is a personalized query.
 21. A systemcomprising: one or more processors; and a memory coupled to theprocessors comprising instructions executable by the processors, theprocessors operable when executing the instructions to: score a firstset of content objects of a plurality of content objects of an onlinesocial network based on one or more user-engagement factors; identifyone or more related content objects, wherein each related contentobjects is connected within the online social network to one or morecontent objects of the first set of content objects having a scoregreater than a threshold score; generate a plurality of structuredqueries that each comprise references to one or more content objects ofthe plurality of content objects, wherein at least one of the structuredqueries is a personalized query comprising a reference to at least oneof the related content objects; and send, to a client device of a firstuser, instructions for presenting one or more of the generatedstructured queries to the first user for display on an interfacecurrently accessed by the first user, wherein at least one of the sentstructured queries is a personalized query.